tasks/task_039_comprehensive_decision_medium_medium014.md

---
id: task_039_comprehensive_decision_medium_medium014
name: comprehensive_decision-medium-medium014
category: comprehensive_decision
grading_type: llm_judge
timeout_seconds: 1200
gold_file: qa_gold/comprehensive_decision/medium014.json
workspace_files: [
  {
    "source": "database/bilingual_translation_english_chinese.json",
    "dest": "database/bilingual_translation_english_chinese.json"
  },
  {
    "source": "database/enterprise/company_core.csv",
    "dest": "database/enterprise/company_core.csv"
  },
  {
    "source": "database/enterprise/company_operation_status.csv",
    "dest": "database/enterprise/company_operation_status.csv"
  },
  {
    "source": "database/enterprise/company_operation_status_detail.csv",
    "dest": "database/enterprise/company_operation_status_detail.csv"
  },
  {
    "source": "database/enterprise/company_operation_yearly_status.csv",
    "dest": "database/enterprise/company_operation_yearly_status.csv"
  },
  {
    "source": "database/enterprise/company_profile.csv",
    "dest": "database/enterprise/company_profile.csv"
  },
  {
    "source": "database/enterprise/company_profile_as.csv",
    "dest": "database/enterprise/company_profile_as.csv"
  },
  {
    "source": "database/enterprise/company_profile_eu.csv",
    "dest": "database/enterprise/company_profile_eu.csv"
  },
  {
    "source": "database/enterprise/company_profile_na.csv",
    "dest": "database/enterprise/company_profile_na.csv"
  },
  {
    "source": "database/enterprise/company_profile_oc.csv",
    "dest": "database/enterprise/company_profile_oc.csv"
  },
  {
    "source": "database/industry/national_industry_status.csv",
    "dest": "database/industry/national_industry_status.csv"
  },
  {
    "source": "database/industry/national_industry_status_detail.csv",
    "dest": "database/industry/national_industry_status_detail.csv"
  },
  {
    "source": "database/industry/national_industry_yearly_status.csv",
    "dest": "database/industry/national_industry_yearly_status.csv"
  },
  {
    "source": "database/industry/regional_industry_status.csv",
    "dest": "database/industry/regional_industry_status.csv"
  },
  {
    "source": "database/industry/regional_industry_status_detail.csv",
    "dest": "database/industry/regional_industry_status_detail.csv"
  },
  {
    "source": "database/industry/regional_industry_yearly_status.csv",
    "dest": "database/industry/regional_industry_yearly_status.csv"
  },
  {
    "source": "database/internal_metrics.csv",
    "dest": "database/internal_metrics.csv"
  },
  {
    "source": "database/policy/policy_release_status.csv",
    "dest": "database/policy/policy_release_status.csv"
  },
  {
    "source": "database/policy/policy_resource.csv",
    "dest": "database/policy/policy_resource.csv"
  }
]
---

## Prompt

In 2022, for the metal smelting and rolling processing industry, among provinces with valid records for both total government subsidies and total industry employee count, per capita subsidy is computed as each province's total government rewards and subsidies divided by that province's industry employee count. What is the per capita subsidy in yuan for the province with the highest per capita subsidy? What is the nationwide rank of the enterprise with the highest such indicator in that province among all enterprises in this industry?

Output guidelines:
Two answers required: first is a numeric value (2 decimal places, unit yuan/person), i.e. the highest provincial per capita subsidy; second is a rank number (integer), indicating the nationwide rank of the enterprise with the highest indicator in that province among all enterprises in this industry. If relevant data cannot be found, respond with "No relevant data found".

Only use files under `./database/`.

## Expected Behavior

Agent should read the provided `database/` files, compute the result, and return the final answer. The final answer must follow the required output format.

## Grading Criteria

- [ ] Final answer semantically matches the gold `answer`.
- [ ] Output format follows `guidelines`.

## LLM Judge Rubric

### Criterion 1: Multi-answer Correctness (Weight: 100%)

Gold answer JSON:
`[17569.95, 12]`

Scoring rules:
- The gold answer is a list with N=2 parts.
- Judge each predicted part against the corresponding gold part by semantic equivalence.
- Return `scores` with `part_0 ... part_1` each as 0 or 1.
- Return `total = (sum(part_i)) / 2` exactly.
- If the model output is missing or cannot be parsed into 2 comparable parts, score all parts 0.