tasks/task_032_comprehensive_decision_medium_medium007.md

---
id: task_032_comprehensive_decision_medium_medium007
name: comprehensive_decision-medium-medium007
category: comprehensive_decision
grading_type: llm_judge
timeout_seconds: 1200
gold_file: qa_gold/comprehensive_decision/medium007.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, analyze government subsidy leverage in the information transmission, software and information technology services industry. Define government subsidy leverage effect as the ratio of each province's total operating profit to total government subsidies. What is the specific ratio for the province with the highest government subsidy leverage effect? Which enterprise in that province has the highest leverage effect?

Output guidelines:
Two answers required: first is a numeric value (2 decimal places, unitless ratio); second is the full company name. 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:
`[67.19, "Dongche Kexin Systems Company"]`

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.