1

README.md

@@ -37,56 +37,306 @@ https://github.com/41-edu/KnowCP
 
 ## Project Overview
 
-KnowCP is a comprehensive benchmark for evaluating multimodal large language models on Chinese painting understanding, from basic recognition to deep reasoning.
+KnowCP is a comprehensive benchmark designed to evaluate multimodal large language models on the understanding of Chinese paintings, ranging from basic recognition tasks to deep reasoning challenges.
 
-This repository is the dataset repository of KnowCP. It stores the image corpus, annotation resources, question sets, and knowledge-base metadata used by the benchmark.
+This repository serves as the dataset repository for KnowCP. It stores the image corpus, annotation resources, question sets, and knowledge-base metadata utilized by the benchmark.
 
 ## Dataset Scale
 
-The following counts are aligned with the benchmark website configuration and public content files.
+The following statistics are aligned with the configuration of the benchmark website and public content files.
 
-1. Paintings: 1210
-2. Images: 2331
-3. Seal annotations: 8690
-4. Colophon or inscription annotations: 2434
-5. Object or element annotations: 5052
-6. Technique annotations: 1312
-7. Total question items used by the benchmark website: 38044
+1. Paintings: 1210 artworks, some of which consist of multiple pages, resulting in a total of 2331 images.
+2. Seal Annotation: 8690 annotations, corresponding to seals on 1792 images, so we constructed 1792 seal recognition questions.
+3. Inscription Annotation: 2434 annotations, corresponding to inscriptions on 2351 images, so we constructed 2351 inscription recognition questions.
+4. Element Annotation: 5048 annotations.
+5. Technique Annotation: 1312 annotations.
+6. Deep Reasoning:
+   - Visual Analysis: 826 questions
+   - Cultural Context: 716 questions
+   - Provenance Research: 834 questions
+7. Total question items used by the benchmark: 24700
 
 ## Repository Contents
 
-1. **images** : All painting images and related sub-images used by the benchmark.
-2. **questions** : All benchmark QA files. We provide 14 question files under **questions/by_type** and each file corresponds to one question source type.
-
-   * All QA content is in Chinese. For concrete QA presentation style and English-facing examples, see:http://localhost:5173/#question-distribution
-   * 14 question files and counts used in the website benchmark view:
-     * **Foundational Knowledge**
-       * ITT and MITT style tasks(1210): identify title-level information from one image or multiple images.
-
-         * ITT is in **questions/by_type/ITT_MHQA_choice.json** and **questions/by_type/ITT_MHQA_fillin.json**
-         * MITT is in **questions/by_type/MITT_MHQA_choice.json** and **questions/by_type/MITT_MHQA_fillin.json**
-       * TTI(1210): retrieve the matching image from title information.
-       * MHQA(4840): multi-step reasoning over image and context, provided in choice and fill-in formats.
-
-         * MHQA is in **questions/by_type/ITT_MHQA_choice.json** , **questions/by_type/ITT_MHQA_fillin.json , questions/by_type/MITT_MHQA_choice.json** and **questions/by_type/MITT_MHQA_fillin.json**
-     * **Visual Content**
-       * SR(1792): seal recognition.
-       * IR(2351): inscription or colophon recognition.
-       * ER(16411): element recognition, provided in multiple-choice and fill-in formats.
-       * TR(2624): painting technique recognition, provided in multiple-choice and fill-in formats.
-     * **Deep Reasoning**
-       * VA(922): visual analysis.
-       * CC(922): cultural context reasoning.
-       * PR(922): provenance research reasoning.
+1. **images**: All painting images and related sub-images used by the benchmark.
+
+2. **questions**: All benchmark QA files. We provide 14 question files under **questions/by_type**, with each file corresponding to one question source type. All QA content is in Chinese. For the specific presentation style of QA and English-facing examples, refer to **Cases** in https://41-edu.github.io/KnowCP-Benchmark/#distribution
+
+   - **Foundational Knowledge**
+
+     - **Image-to-Title (ITT)**: Determine the title of a painting composed of a single page.
+
+       - ITT data is stored in **questions/by_type/ITT_MHQA_choice.jsonl** and **questions/by_type/ITT_MHQA_fillin.jsonl**
+
+       - Example:
+
+         - plaintext
+
+           ```
+           {"qid": "IMG000003-P1-Q1", "image_id": "IMG000003", "phase": "P1", "question_no": "Q1", 
+            "type": "ITT", "answer_format": "single_choice", 
+            "prompt": "请判断该作品标题（中英文对照）是以下哪一项？\n
+                A. 白鸽 / Pigeon\n
+                B. 江村秋晓图 / River Village in Autumn Dawn\n
+                C. 冬景山水图 / Wintry Mountains\n
+                D. 画天中华瑞 / Beautiful Scenery of the Tianzhong\n
+           请只输出选项字母（A/B/C/D）。\n\n输出格式硬约束：\n- 只输出一个选项字母（如 A）。\n- 禁止输出解释、标点、额外文本。", 
+            "ground_truth": "C", 
+            "image_paths": ["images/IMG000003_0.jpg"]}
+           ```
+
+           - `"type": "ITT"` for **ITT** questions
+           - The image uploaded during runtime is specified in `"image_paths": ["images/IMG000003_0.jpg"]`
+           - The question content is in `"prompt"`
+           - The expected answer is in `"ground_truth"`
+
+     - **Multi-Image-to-Title (MITT)**: Determine the title of a painting composed of multiple pages.
+
+       - MITT data is stored in **questions/by_type/MITT_MHQA_choice.jsonl** and **questions/by_type/MITT_MHQA_fillin.jsonl**
+
+       - Example:
+
+         - plaintext
+
+           ```
+           {"qid": "IMG000002-P1-Q1", "image_id": "IMG000002", "phase": "P1", "question_no": "Q1", 
+            "type": "MITT", "answer_format": "single_choice", 
+            "prompt": "请综合观察这些同一作品的多张图像，判断其标题（中英文对照）是以下哪一项？\n
+                A. 春郊游骑图 / A Horseback Trip in a Spring Countryside\n
+                B. 春山青霁 / Clear Skies on Spring Mountain\n
+                C. 螽斯绵瓞 / Melon and Katydid\n
+                D. 山水图（十六开） / Landscapes with Poems (Sixteen Leaves)\n
+           请只输出选项字母（A/B/C/D）。\n\n输出格式硬约束：\n- 只输出一个选项字母（如 A）。\n- 禁止输出解释、标点、额外文本。", 
+            "ground_truth": "D", 
+            "image_paths": ["images/IMG000002_0.jpg", "images/IMG000002_1.jpg", "images/IMG000002_2.jpg"]}
+           ```
+
+           - `"type": "MITT"` for **MITT** questions
+           - The images uploaded during runtime are specified in `"image_paths": ["images/IMG000002_0.jpg", "images/IMG000002_1.jpg", "images/IMG000002_2.jpg"]`
+           - The question content is in `"prompt"`
+           - The expected answer is in `"ground_truth"`
+
+     - **Title-to-Image (TTI)**: Select the image that matches a given title from multiple images.
+
+       - Example:
+
+         - plaintext
+
+           ```
+           {"qid": "IMG000001-P1-Q6", "image_id": "IMG000001", "phase": "P1", "question_no": "Q6", 
+           "type": "TTI", "answer_format": "single_choice", 
+           "prompt": "已知作品标题为：山水图（十二开） / Landscapes and Trees (Twelve Leaves)。请判断它对应下列四张图中的哪一张？\n
+               A. 图像A\n
+               B. 图像B\n
+               C. 图像C\n
+               D. 图像D\n
+           请只输出选项字母（A/B/C/D）。\n\n输出格式硬约束：\n- 只输出一个选项字母（如 A）。\n- 禁止输出解释、标点、额外文本。", 
+           "ground_truth": "A", 
+           "image_paths": ["images/IMG000001_0.jpg", "images/IMG000002_0.jpg", "images/IMG000003_0.jpg", "images/IMG000004_0.jpg"]}
+           ```
+
+           - `"type": "TTI"` for **TTI** questions
+           - The images uploaded during runtime are specified in `"image_paths": ["images/IMG000001_0.jpg", "images/IMG000002_0.jpg", "images/IMG000003_0.jpg", "images/IMG000004_0.jpg"]}`
+           - The question content is in `"prompt"`
+           - The expected answer is in `"ground_truth"`
+
+     - **Multi-hop-QA (MHQA)**: QA on the metadata of each painting.
+
+       - MHQA data is stored in **questions/by_type/ITT_MHQA_choice.jsonl**, **questions/by_type/ITT_MHQA_fillin.jsonl**, **questions/by_type/MITT_MHQA_choice.jsonl** and **questions/by_type/MITT_MHQA_fillin.jsonl**
+
+       - MHQA includes metadata QA for paintings with multiple pages and single pages. This QA is a sequential question-and-answer process covering four aspects: author, dynasty, museum, and medium. When asking a new question, we append the context of the previous question to the prompt.
+
+       - Example:
+
+         - plaintext
+
+           ```
+           {"qid": "IMG000003-P1-Q2", "image_id": "IMG000003", "phase": "P1", "question_no": "Q2", 
+           "type": "ITT_MHQA_choice", "answer_format": "single_choice", 
+           "prompt": "该作品的作者是哪一项？\nA. 龚贤\nB. 樊圻\nC. 吴宏\nD. 高岑\n请只输出选项字母（A/B/C/D）。\n\n输出格式硬约束：\n- 只输出一个选项字母（如 A）。\n- 禁止输出解释、标点、额外文本。", 
+           "ground_truth": "A", 
+           "image_paths": ["images/IMG000003_0.jpg"]}
+           
+           {"qid": "IMG000003-P1-Q3", "image_id": "IMG000003", "phase": "P1", "question_no": "Q3", 
+           "type": "ITT_MHQA_choice", "answer_format": "single_choice", 
+           "prompt": "该作品的朝代是哪一项？\nA. 清\nB. 晋\nC. 金\nD. 明\n请只输出选项字母（A/B/C/D）。\n\n输出格式硬约束：\n- 只输出一个选项字母（如 A）。\n- 禁止输出解释、标点、额外文本。", 
+           "ground_truth": "A", 
+           "image_paths": ["images/IMG000003_0.jpg"]}
+           
+           {"qid": "IMG000003-P1-Q4", "image_id": "IMG000003", "phase": "P1", "question_no": "Q4", 
+           "type": "ITT_MHQA_choice", "answer_format": "single_choice", 
+           "prompt": "该作品的收藏机构为哪一项？\nA. 佛利尔·赛克勒美术馆\nB. 东京艺术大学\nC. 东京国立博物馆\nD. 大都会博物馆\n请只输出选项字母（A/B/C/D）。\n\n输出格式硬约束：\n- 只输出一个选项字母（如 A）。\n- 禁止输出解释、标点、额外文本。", 
+           "ground_truth": "D", 
+           "image_paths": ["images/IMG000003_0.jpg"]}
+           
+           {"qid": "IMG000003-P1-Q5", "image_id": "IMG000003", "phase": "P1", "question_no": "Q5", 
+           "type": "ITT_MHQA_choice", "answer_format": "single_choice", 
+           "prompt": "该作品的材质是哪一项？\nA. 棉布\nB. 绫本\nC. 纸本\nD. 金笺\n请只输出选项字母（A/B/C/D）。\n\n输出格式硬约束：\n- 只输出一个选项字母（如 A）。\n- 禁止输出解释、标点、额外文本。", 
+           "ground_truth": "C", 
+           "image_paths": ["images/IMG000003_0.jpg"]}
+           ```
+
+         - This is the structure of an entire sequence of MHQA questions. This type includes four sub-types: `ITT_MHQA_choice`, `ITT_MHQA_fillin`, `MITT_MHQA_choice`, and `MITT_MHQA_fillin`.
+
+         - The following is an example of a prompt with appended context:
+
+           - plaintext
+
+             ```
+             {"qid": "IMG000003-P1-Q2", "image_id": "IMG000003", "phase": "P1", "question_no": "Q2", 
+             "type": "ITT_MHQA_choice", "answer_format": "single_choice", 
+             "prompt": "以下是同一作品前序问答，请结合上下文回答当前问题：\n
+                 第1轮问题：\n请判断该作品标题（中英文对照）是以下哪一项？\nA. 瓮牖图 / Round Window Scroll\nB. 秋江钓艇 / Fishing Boat on Autumn River\nC. 归渔图 / Returning Fishermen\nD. 冬景山水图 / Wintry Mountains\n请只输出选项字母（A/B/C/D）。\n\n输出格式硬约束：\n- 只输出一个选项字母（如 A）。\n- 禁止输出解释、标点、额外文本。\n
+                 第1轮回答：D\n
+                 当前问题：\n该作品的作者是哪一项？\nA. 龚贤\nB. 樊圻\nC. 吴宏\nD. 高岑\n请只输出选项字母（A/B/C/D）。\n\n输出格式硬约束：\n- 只输出一个选项字母（如 A）。\n- 禁止输出解释、标点、额外文本。", 
+             "ground_truth": "A", 
+             "image_paths": ["images/IMG000003_0.jpg"], 
+             "request_image_paths": ["images\\IMG000003_0.jpg"], "answer": "A", "raw_answer": "A", "answer_structured": {"text": "A"}, "error": "", "skipped": false}
+             ```
+
+   - **Visual Content**
+
+     - **Seal-Recognition (SR)**
+
+       - Example
+
+         - plaintext
+
+           ```
+           {"qid": "IMG000001-P2-Q1-Q01-IMG01", "image_id": "IMG000001", "phase": "P2", "question_no": "Q1", 
+           "type": "SR", "answer_format": "json", 
+           "prompt": "请检测这张图中的所有印章。\n输出必须是 JSON 数组，每个元素代表一个印章。\n每个元素至少包含字段：bbox、text。\n- bbox: 印章边界框坐标（具体坐标格式将由系统补充说明）。\n- text: 该印章印文；无法识别时可填空字符串。\n- text 字段请统一输出简体中文；若识别到繁体或异体字，请在不改变语义的前提下尽量转换为简体。\n请只输出 JSON，不要输出解释。\n\n输出格式硬约束：\n- 只输出合法 JSON（对象或数组）。\n- 禁止输出 Markdown 代码块、解释性文字。\\n\\n坐标协议：\nbbox 使用 [x_min, y_min, x_max, y_max]，坐标为 0~1 归一化浮点数（voc_xyxy_norm_01）。\n输出约束：\n1) 仅输出 JSON 数组。\n2) 每个元素必须包含 bbox 和 text 字段。\n3) 字段名必须是 text，不要使用 text_content / textContent。\n4) 坐标必须满足 x_min < x_max 且 y_min < y_max。\n5) 未检测到目标时输出 []。", 
+           "ground_truth": {"task": "seal_detection", "items": [{"bbox": [0.587156, 0.735297, 0.630989, 0.85107], "text": "印章", "sub_image_path": "images/IMG000001_0.jpg"}, {"bbox": [0.01631, 0.830936, 0.057085, 0.926575], "text": "印章", "sub_image_path": "images/IMG000001_0.jpg"}], "texts_from_seals_json": ["印章"], "image_paths": ["images/IMG000001_0.jpg"]}, 
+           "image_paths": ["images/IMG000001_0.jpg"]}
+           ```
+
+         - The question is in `"prompt"`, which requires the model to output the bounding boxes of all seals in the image.
+
+         - The expected answer is in `"ground_truth"`, which contains the annotation results from our annotators as the ground truth.
+
+     - **Inscription-Recognition (IR)**
+
+       - Example
+
+         - plaintext
+
+           ```
+           {"qid": "IMG000012-P2-Q2-IMG01", "image_id": "IMG000012", "phase": "P2", "question_no": "Q2", 
+           "type": "IR", "answer_format": "json", 
+           "prompt": "请检测这张图中的所有题跋/款识文字区域。\n输出必须是 JSON 数组，每个元素代表一个题跋区域。\n每个元素至少包含字段：bbox、text。\n- bbox: 题跋区域边界框（具体坐标格式将由系统补充说明）。\n- text: 该区域识别出的题跋文字；无法识别时可填空字符串。\n- text 字段请统一输出简体中文；若识别到繁体或异体字，请在不改变语义的前提下尽量转换为简体。\n请只输出 JSON，不要输出解释。\n\n输出格式硬约束：\n- 只输出合法 JSON（对象或数组）。\n- 禁止输出 Markdown 代码块、解释性文字。\\n\\n坐标协议：\nbbox 使用 [x_min, y_min, x_max, y_max]，坐标为 0~1 归一化浮点数（voc_xyxy_norm_01）。\n输出约束：\n1) 仅输出 JSON 数组。\n2) 每个元素必须包含 bbox 和 text 字段。\n3) 字段名必须是 text，不要使用 text_content / textContent。\n4) 坐标必须满足 x_min < x_max 且 y_min < y_max。\n5) 未检测到目标时输出 []。", 
+           "ground_truth": {"task": "inscription_detection", "items": [{"bbox": [0.076534, 0.037318, 0.199841, 0.249891], "text": "太古蛰龙醒，蚕丛霹雳开。五溪云不去，三峡雪飞来。瞿山清。", "text_gt_available": true, "sub_image_path": "images/IMG000012_0.jpg"}], "kb_inscription": "太古蛰龙醒，蚕丛霹雳开。五溪云不去，三峡雪飞来。瞿山清。", "image_paths": ["images/IMG000012_0.jpg"]}, 
+           "image_paths": ["images/IMG000012_0.jpg"]}
+           ```
+
+         - The question is in `"prompt"`, which requires the model to output the bounding boxes of all inscriptions in the image.
+
+         - The expected answer is in `"ground_truth"`, which contains the annotation results from our annotators as the ground truth.
+
+     - **Element-Recognition (ER)**: Provided in multiple-choice and fill-in formats.
+
+       - Example
+
+         - plaintext
+
+           ```
+           {"qid": "IMG000001-P2-Q3_O001_L1", "image_id": "IMG000001", "phase": "P2", "question_no": "Q3_O001_L1", 
+           "type": "ER_choice", "answer_format": "single_choice", 
+           "prompt": "请观察红框中的主体物象，判断其一级类别。\n\n要求：\n1）只依据红框内容作答，不参考画面其他区域。\n2）本题为单选题，只输出一个选项字母（如 A）。\n\n选项：\nA. 植物\nB. 动物\nC. 人物\nD. 神佛\nE. 建筑\nF. 家具\nG. 交通\nH. 文玩\nI. 乐器\nJ. 水\nK. 峰峦\nL. 云雾\nM. 地形\nN. 环境\nO. 器物杂具\nP. 服饰纹样\nQ. 民俗节庆\n\n输出格式硬约束：\n- 只输出一个选项字母（如 A）。\n- 禁止输出解释、标点、额外文本。", 
+           "ground_truth": "E", 
+           "image_paths": ["images/IMG000001_4.jpg"], "focus_bbox": [213.36073997944501, 233.20400194911036, 384.7893114080165, 407.0991715277332], "focus_image_index": 5, "focus_sub_image_path": "images/IMG000001_4.jpg"}
+           ```
+
+         - The uploaded materials include the image itself (`"image_paths": ["images/IMG000001_4.jpg"]`) and a red annotation box (`"focus_bbox": [213.36073997944501, 233.20400194911036, 384.7893114080165, 407.0991715277332]`).
+
+         - The question is in `"prompt"`, which requires the model to identify the object within the given red box.
+
+         - The expected answer is in `"ground_truth"`.
+
+     - **Technique-Recognition (TR)**: Provided in multiple-choice and fill-in formats.
+
+       - Example
+
+         - plaintext
+
+           ```
+           {"qid": "IMG000001-P3-Q1_R001", "image_id": "IMG000001", "phase": "P3", "question_no": "Q1_R001", "type": "TR_choice", "answer_format": "single_choice", "prompt": "请只根据红框区域中的笔法与皴法特征作答。\n这是单选题。\nA. 披麻皴\nB. 解索皴\nC. 荷叶皴\nD. 牛毛皴\nE. 斧劈皴\nF. 雨点皴\nG. 拖泥带水皴\nH. 乱柴皴\nI. 弹涡皴\nJ. 卷云皴\nK. 米点皴\nL. 折带皴\n只输出一个选项字母，不要解释。\n\n输出格式硬约束：\n- 只输出一个选项字母（如 A）。\n- 禁止输出解释、标点、额外文本。", "ground_truth": "A", "image_paths": ["images/IMG000001_0.jpg"], "focus_bbox": [158.2181562604731, 537.6836388751759, 783.4618818175318, 727.151434498527], "focus_image_index": 1, "focus_sub_image_path": "images/IMG000001_0.jpg"}
+           ```
+
+         - The uploaded materials include the image itself (`"image_paths": ["images/IMG000001_4.jpg"]`) and a red annotation box (`"focus_bbox": [213.36073997944501, 233.20400194911036, 384.7893114080165, 407.0991715277332]`).
+
+         - The question is in `"prompt"`, which requires the model to identify the painting technique used in the given red box.
+
+         - The expected answer is in `"ground_truth"`.
+
+   - **Deep Reasoning**
+
+     - **Visual-Analysis (VA)**
+
+       - Example
+
+         - plaintext
+
+           ```
+           {"qid": "IMG000002-P4-Q3A", "image_id": "IMG000002", "phase": "P4", "question_no": "Q3A", 
+           "type": "VA", "answer_format": "text", 
+           "prompt": "请客观描述画面主要内容、主体关系与关键视觉要素。\n要求：\n1）仅基于图像本身描述，不引入外部史料。\n2）控制在 200 字以内。\n3）优先覆盖主体、动作、空间关系。\n\n输出格式硬约束：\n- 仅输出最终答案，不要解释。", 
+           "ground_truth": "本册笔墨清雅，描摹精妙，所写奇山异水各具风致。第一开绘一座半岛伸入湖中，三面环水，对岸群山间垂瀑而下，景物安排繁简得当，形成含蓄悠远的空间效果。第三开写近处山泉、石桥，对岸层峦叠嶂，半山有洞门半开，境界如仙。第六开则绘江边高台，四面空阔，山光与清风之感相互生发，景象疏朗高远。\n\n全册山石树木多经反复烘染，呈现出阴阳分明而又朦胧混沌的视觉效果。程正揆所谓“用笔墨如龙驭风，如云行空，隐现变幻”，点出了其笔墨不以雄强取胜、而以韵致见长的特点。这种不可捉摸、恍惚迷幻的笔墨表现，构成了龚贤绘画特有的意境。", 
+           "image_paths": ["images/IMG000002_0.jpg", "images/IMG000002_1.jpg", "images/IMG000002_2.jpg"]}
+           ```
+
+         - The question is in `"prompt"`, which requires the model to provide a textual description of the visual information of the painting.
+
+         - The expected answer is in `"ground_truth"`, which is a carefully compiled text by experts in Chinese painting.
+
+     - **Cultural-Context (CC)**
+
+       - Example
+
+         - plaintext
+
+           ```
+           {"qid": "IMG000002-P4-Q3B", "image_id": "IMG000002", "phase": "P4", "question_no": "Q3B", 
+           "type": "CC", "answer_format": "text", 
+           "prompt": "请结合题材来源与图文对应关系，解释这幅作品的文化含义。\n要求：\n1）说明图像元素与题材文本的对应。\n2）控制在 220 字以内。\n3）避免泛泛而谈。\n\n输出格式硬约束：\n- 仅输出最终答案，不要解释。", 
+           "ground_truth": "第六开涉及昭明太子题材。昭明太子萧统在文学史上享有盛名，南京、镇江、常熟等地都流传其读书遗迹，虽多有附会，但这一文化记忆为画中“江边高台、四面空阔”的景象提供了可供联想的人文背景，使画面不仅是山水描写，也带有追怀古人、寄托文雅想象的意味。\n\n有研究者对册中诗题作过考释，认为此册“诗画参读，极耐玩味”，体现出画与诗的紧密结合。册后两开画跋又集中阐述龚贤关于“作画”与“识画”关系的理论，强调“未学画先知看画”，将“看画”视为作画精进的重要前提。这些诗题与跋文共同构成了理解此册的重要文本资源，有助于从题咏、画论与观画方法的层面把握作品意涵。", 
+           "image_paths": ["images/IMG000002_0.jpg", "images/IMG000002_1.jpg", "images/IMG000002_2.jpg"]}
+           ```
+
+         - The question is in `"prompt"`, which requires the model to provide an explanation of the cultural background of the painting.
+
+         - The expected answer is in `"ground_truth"`, which is a document verified by experts in Chinese painting through multiple sources.
+
+     - **Provenance-Research (PR)**
+
+       - Example
+
+         - plaintext
+
+           ```
+           {"qid": "IMG000002-P4-Q3C", "image_id": "IMG000002", "phase": "P4", "question_no": "Q3C", 
+           "type": "PR", "answer_format": "text", 
+           "prompt": "请概述这幅作品的版本流传、归属争议与断代判断。\n要求：\n1）覆盖款识/著录/收藏或学界判断中的关键点。\n2）控制在 200 字以内。\n3）结论需有依据，不要编造来源。\n\n输出格式硬约束：\n- 仅输出最终答案，不要解释。", "ground_truth": "本册绘于康熙二十七年（1688），为龚贤去世前一年的重要作品之一，在其晚年创作中具有代表性。程正揆称龚贤“盖以韵胜，不以力雄者也”，这一评价也提示了此册在龚贤艺术风格中的位置，即以“韵胜”为区别于同时代画家的重要特征。\n\n册后跋文提及宋代宣和内府、倪瓒清閟阁、顾瑛玉山草堂等历史著名藏画之所，虽主要用于申说“识画”理论，但同时关联到古代鉴藏传统与绘画鉴识史的脉络。据钤印可知，本册曾经金城、张学良等人鉴藏。", 
+           "image_paths": ["images/IMG000002_0.jpg", "images/IMG000002_1.jpg", "images/IMG000002_2.jpg"]}
+           ```
+
+         - The question is in `"prompt"`, which requires the model to provide a textual description of the provenance of the painting.
+
+         - The expected answer is in `"ground_truth"`, which is a document verified by experts in Chinese painting through multiple sources.
+
 3. **annotations**
+
    Fine-grained annotations produced by our annotators for each painting image, including seals, inscriptions, elements, and techniques.
 
-   * Annotation visualization references : http://localhost:5173/#distribution
+   - Annotation visualization references: https://41-edu.github.io/KnowCP-Benchmark/#distribution
+
 4. **kb**
+
    Core metadata per painting, including identity and background attributes used by benchmark tasks.
 
 ## If You Want to Run Evaluation
 
 This repository provides data only.
+
 For actual model benchmarking and score computation, please use the script repository:
+
 https://github.com/41-edu/KnowCP

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