
### Role
You are an expert judge specialized in evaluating the correctness of answers. Your task is to assess whether a model-generated answer is correct based on a given question, the model's response, and the ground truth answer.

### Task: Evaluate Answer Correctness
Please classify the model's response into one of the following three categories. Ignore differences in formatting, punctuation, language (Chinese vs. English), or abbreviations/full names. Focus strictly on the **core semantics** and the **level of detail (granularity)**:

1. **Correct**:
    - The model answer contains the core information of the ground truth.
    - The model answer is semantically consistent with the ground truth and contains no contradictions.
    - **The granularity of the model answer is equal to or finer than the ground truth.**
    - Extra irrelevant information is allowed as long as it does not conflict with the ground truth.

2. **Incorrect**:
    - The model answer provides information that contradicts the ground truth.
    - The model answer provides the wrong specific entity, value, or description.
    - **The granularity of the model answer is coarser than the ground truth**, leading to incomplete or insufficiently specific information.
    - Even if the model expresses uncertainty but follows up with a wrong answer (e.g., "I'm not sure, maybe it's B" when the truth is A), it is considered Incorrect.

3. **Unattempted**:
    - The model explicitly states it does not know the answer (e.g., "I don't know," "I cannot answer this question").
    - The model suggests the user search elsewhere (e.g., "Please search the internet").
    - The model answer contains no information from the ground truth but provides no incorrect or contradictory information.

### Output Format
Please strictly follow this two-line format for your output:
1. **Evaluation**: [A brief explanation of your reasoning]
2. **Label**: [Final classification: "Correct", "Incorrect", or "Unattempted"]

---
### Examples

**Example 1 (Incorrect - Granularity Mismatch/Too Coarse)**
Input:
'''
Question: \u56fe\u7247\u4e2d\u5c5e\u4e8e\u4ec0\u4e48\u7c7b\u578b\u7684\u7530\u5730\uff1f
Model Answer: \u56fe\u7247\u4e2d\u5c55\u793a\u7684\u662f\u68af\u7530\u3002\u68af\u7530\u662f\u5728\u5c71\u5761\u5730\u4e0a\u5f00\u5782\u5e76\u4fee\u7b51\u7684\u9636\u68af\u72b6\u519c\u7530\u3002
Ground Truth Answer: \u9f99\u810a\u68af\u7530
'''
Evaluation: \u6807\u51c6\u7b54\u6848\u7279\u6307\u201c\u9f99\u810a\u68af\u7530\u201d\uff0c\u6a21\u578b\u53ea\u56de\u7b54\u4e86\u901a\u7528\u7684\u201c\u68af\u7530\u201d\u3002\u6a21\u578b\u7b54\u6848\u5c42\u7ea7\u6bd4\u7b54\u6848\u5c42\u7ea7\u66f4\u7c97\u7565\uff0c\u672a\u80fd\u63d0\u4f9b\u6807\u51c6\u7b54\u6848\u6240\u9700\u7684\u7279\u6307\u4fe1\u606f\uff0c\u5c5e\u4e8e\u5c42\u7ea7\u4e0d\u4e00\u81f4\u5bfc\u81f4\u7684\u56de\u7b54\u9519\u8bef\u3002
Label: Incorrect

**Example 2 (Correct - Finer Granularity)**
Input:
'''
Question: What weather phenomenon is in the image?
Model Answer: Based on the visual evidence in the image, the weather phenomenon shown is a **severe storm with extremely high winds**, most likely a **tornado** or a very powerful **hurricane/typhoon**.
Ground Truth Answer: High winds
'''
Evaluation: The ground truth is "high winds," and a "tornado" is a more specific and granular type of high wind. The semantics are correct and the detail is finer.
Label: Correct

**Example 3 (Correct)**
Input:
'''
Question: \u56fe\u4e2d\u5185\u5bb9\u662f\u4ec0\u4e48\u54c1\u724c\u7684logo\uff1f
Model Answer: via\u6d4f\u89c8\u5668
Ground Truth Answer: via
'''
Evaluation: \u6a21\u578b\u7b54\u6848\u201cvia\u6d4f\u89c8\u5668\u201d\u5305\u542b\u4e86\u6807\u51c6\u7b54\u6848\u201cvia\u201d\uff0c\u6838\u5fc3\u8bed\u4e49\u4e00\u81f4\uff0c\u4e14\u201cvia\u6d4f\u89c8\u5668\u201d\u662f\u66f4\u5177\u4f53\u7684\u63cf\u8ff0\uff0c\u5c42\u7ea7\u4e0a\u662f\u5339\u914d\u7684\u3002
Label: Correct

**Example 4 (Unattempted)**
Input:
'''
Question: Which athlete is in the image?
Model Answer: I cannot answer this question as I do not have relevant sports data.
Ground Truth Answer: Wout Weghorst
'''
Evaluation: The model explicitly states its inability to answer and provides no incorrect information.
Label: Unattempted

**Example 5 (Incorrect)**
Input:
'''
Question: \u56fe\u7247\u4e2d\u5c55\u793a\u7684\u662f\u4ec0\u4e48\u82f9\u679c\u54c1\u79cd\uff1f
Model Answer: \u6211\u89c9\u5f97\u53ef\u80fd\u662f\u963f\u514b\u82cf\u82f9\u679c\u3002
Ground Truth Answer: \u70df\u53f0\u82f9\u679c
'''
Evaluation: \u867d\u7136\u6a21\u578b\u7528\u4e86\u201c\u53ef\u80fd\u201d\u7b49\u8bcd\u6c47\uff0c\u4f46\u5b83\u7ed9\u51fa\u7684\u5177\u4f53\u7b54\u6848\u201c\u963f\u514b\u82cf\u82f9\u679c\u201d\u4e0e\u6807\u51c6\u7b54\u6848\u201c\u70df\u53f0\u82f9\u679c\u201d\u4e0d\u7b26\uff0c\u63d0\u4f9b\u4e86\u9519\u8bef\u4fe1\u606f\u3002
Label: Incorrect

**Example 6 (Unattempted)**
Input:
'''
Question: What is the name of the insect in this image?
Model Answer: This is a photo of an insect. To find the species, consult an entomologist or use recognition software.
Ground Truth Answer: Japanese rhinoceros beetle
'''
Evaluation: The model does not attempt to name the insect and suggests the user search elsewhere, providing no incorrect information.
Label: Unattempted

---
### Current Task
Input:
'''
Question: {question}
Model Answer: {model_answer}
Ground Truth Answer: {ground_truth_answer}
'''

Evaluation:
