You are required to act as an answer evaluator. Given an issue context, the hint disclosed to the agent and the answer from the agent, 
you should rate the performance of the agent into three levels: "failed", "partially", and "success". The rating rules are as follows:

<rules>
1. You will be given a list of <metrics>, for each metric, you should rate in [0,1] for the agent based on the metric criteria, and then multiply the rating by the weight of the metric.
2. If the sum of the ratings is less than 0.45, then the agent is rated as "failed"; if the sum of the ratings is greater than or equal to 0.45 and less than 0.85, then the agent is rated as "partially"; if the sum of the ratings is greater than or equal to 0.85, then the agent is rated as "success".
3. **<text>** means the text is important and should be paid attention to.
4. ****<text>**** means the text is the most important and should be paid attention to.
</rules>

The <metrics> are as follows:
{
    "m1": {
        "criteria": "Precise Contextual Evidence:
            1. The agent must accurately identify and focus on the specific issue mentioned in the context. This involves a close examination of the exact evidence given and determining whether it aligns with the content described in the issue and the involved files.
            2. Always ask yourself: Have the agent provided correct and detailed context evidence to support its finding of issues? If the agent just gives some general description without specifically pointing out where the issues occur, you should give it a low rate.
            3. Once the agent has correctly spotted **** all the issues in <issue> and provided accurate context evidence ****, it should be given a ****full score (1.0) even if it includes other unrelated issues/examples ****"
            4. If the agent has only spotted part of the issues with the relevant context in <issue>, then you should give a medium rate.
            5. The expression in the answer of the agent might not directly pinpoint the issue, but when its answer implies the existence of the <issue> and has provided correct evidence context, then it should be given a high rate for m1.
            6. For issues about something missing and having no clear location information, even if there is context in <issue> involved files, it's ok for the agent to only give an issue description without pointing out where the issue occurs in detail."
         "weight": 0.8,
        "range": [0, 1]
    },
    "m2": {
        "criteria": "Detailed Issue Analysis: 
            1. The agent must provide a detailed analysis of the issue, showing an understanding of how this specific issue could impact the overall task or dataset as human evaluators do.
            2. This metric stresses the importance of not just identifying that there is an issue but also understanding and explaining its implications in detail, rather than simply repeating the information in hint.",
        "weight": 0.15,
        "range": [0, 1]
    },
    "m3": {
        "criteria": "Relevance of Reasoning: 
            1. The agent’s reasoning should directly relate to the specific issue mentioned, highlighting the potential consequences or impacts.
            2. This metric ensures that the agent’s logical reasoning directly applies to the problem at hand, rather than being a generic statement.",
        "weight": 0.05,
        "range": [0, 1]
    }
}
</metrics>

--------------------------------

Now let’s begin:

<issue>
title: ogbl-collab is not hete graph, but says true in metadata.json
content: ogbl-collab is not hete graph, but says true in metadata.json
involved: [
    {
        "name": "metadata.json",
        "context": "The is_heterogeneous attribute should be false for ogbl-collab dataset."
    },
    {
        "name": "README.md",
        "context": "## Dataset Description
 The ogbl-collab dataset is an undirected graph, representing a subset of the collaboration network between authors indexed by MAG[1]. Each node represents an author and edges indicate the collaboration between authors. All nodes come with 128-dimensional features, obtained by averaging the word embeddings of papers that are published by the authors. All edges are associated with two meta-information: the year and the edge weight, representing the number of co-authored papers published in that year. The graph can be viewed as a dynamic multi-graph since there can be multiple edges between two nodes if they collaborate in more than one year."
    }
]
</issue>

<hint>
incorrect dataset property value
</hint>

-------------------- Below is the answer from the agent. Ensure you don’t take the information above as the agent’s answer!

<answer>
Here are the potential issues identified in the uploaded datasets based on the hint:

### Issue 1

```json
{
    "issue": "Missing 'val_neg' and 'test_neg' properties in JSON file",
    "evidence": "{\n    \"description\": \"The task is to predict the future author collaboration relationships given the past collaborations. The goal is to rank true collaborations higher than false collaborations. Specifically, we rank each true collaboration among a set of 100,000 randomly-sampled negative collaborations, and count the ratio of positive edges that are ranked at K-place or above (Hits@K). We found K = 50 to be a good threshold in our preliminary experiments.\",\n    \"type\": \"TimeDependentLinkPrediction\",\n    \"feature\": [\n        \"Node/NodeFeature\",\n        \"Edge/EdgeWeight\"\n    ],\n    \"time\": \"Edge/EdgeYear\",\n    \"train_time_window\": [1963, 2018],\n    \"val_time_window\": [2018, 2019],\n    \"test_time_window\": [2019, 2020]\n}",
    "description": "The 'task_time_dependent_link_prediction_1.json' file is missing the 'val_neg' and 'test_neg' properties which are present in 'task_time_dependent_link_prediction_2.json'. These properties specify the files and keys for validation and test negatives, respectively. The absence of these properties can lead to inconsistencies in the dataset usage."
}
```

### Issue 2

```json
{
    "issue": "Incorrect property key '_Edge' in metadata.json",
    "evidence": "{\n    \"Edge\": {\n        \"_Edge\": {\n            \"file\": \"ogbl-collab.npz\",\n            \"key\": \"edge\"\n        },\n        \"EdgeWeight\": {\n            \"description\": \"Number of co-authored papers published in that year\",\n            \"type\": \"int\",\n            \"format\": \"Tensor\",\n            \"file\": \"ogbl-collab.npz\",\n            \"key\": \"edge_weight\"\n        },\n        \"EdgeYear\": {\n            \"description\": \"Year of the collaboration represented by the Edge\",\n            \"type\": \"int\",\n            \"format\": \"Tensor\",\n            \"file\": \"ogbl-collab.npz\",\n            \"key\": \"edge_year\"\n        }\n    }",
    "description": "In the 'metadata.json' file, the property key '_Edge' under 'Edge' is inconsistent with other property names as it has a leading underscore. This naming inconsistency might cause issues in accessing or processing the dataset properties."
}
```

### Issue 3

```json
{
    "issue": "Mismatched description of dataset in metadata.json",
    "evidence": "{\n    \"description\": \"OGBL-COLLAB dataset.\",\n    \"data\": {\n        \"Node\": {\n            \"NodeFeature\": {\n                \"description\": \"Node features of ogbl-collab dataset.\",\n                \"type\": \"float\",\n                \"format\": \"Tensor\",\n                \"file\": \"ogbl-collab.npz\",\n                \"key\": \"node_feats\"\n            }\n        },\n        \"Edge\": {\n            \"_Edge\": {\n                \"file\": \"ogbl-collab.npz\",\n                \"key\": \"edge\"\n            },\n            \"EdgeWeight\": {\n                \"description\": \"Number of co-authored papers published in that year\",\n                \"type\": \"int\",\n                \"format\": \"Tensor\",\n                \"file\": \"ogbl-collab.npz\",\n                \"key\": \"edge_weight\"\n            },\n            \"EdgeYear\": {\n                \"description\": \"Year of the collaboration represented by the Edge\",\n                \"type\": \"int\",\n                \"format\": \"Tensor\",\n                \"file\": \"ogbl-collab.npz\",\n                \"key\": \"edge_year\"\n            }\n        },\n        \"Graph\": {\n            \"_NodeList\": {\n                \"file\": \"ogbl-collab.npz\",\n                \"key\": \"node_list\"\n            },\n            \"_EdgeList\": {\n                \"file\": \"ogbl-collab.npz\",\n                \"key\": \"edge_list\"\n            }\n         }\n     },\n     \"citation\": \"@inproceedings{wang2020microsoft,\\ntitle={Microsoft academic graph: When experts are not enough},\\nauthor={Wang, Kuansan and Shen, Zhihong and Huang, Chiyuan and Wu, Chieh-Han and Dong, Yuxiao and Kanakia, Anshul},\\nbooktitle={Quantitative Science Studies},\\npages={396--413},\\nyear={2020}\\n}\",\n     \"is_heterogeneous\": true\n }",
    "description": "The description provided in 'metadata.json' is quite minimal and doesn't offer comprehensive insight into the OGBL-COLLAB dataset. A more detailed description, possibly matching the task descriptions provided in 'task_time_dependent_link_prediction' JSON files, would be more informative for users."
}
```

These issues highlight inconsistencies and potential errors that could impact the usability and understanding of the dataset.
</answer>

-------------------- 

response below, 
1. after your analysis, remember to give a **"decision: [failed/partially/success]"** for me to extract it using REGEX.
2. Don't use Code Interpreter!; Use your ability to analyze the text.  ** Pay attention to your calculations and make sure they are correct. **
3. There could be multiple issues described in <issue> part. You should start by thinking clearly about how many issues exist in <issue> and list them out, then you should compare them with the answer from the agent.
4. you should focus on whether the agent has spotted the issue in <issue> rather than caring about whether the agent includes unrelated examples not present in the context.
