tasks/task_019_comprehensive_decision_hard_hard013.md

---
id: task_019_comprehensive_decision_hard_hard013
name: comprehensive_decision-hard-hard013
category: comprehensive_decision
grading_type: llm_judge
timeout_seconds: 1200
gold_file: qa_gold/comprehensive_decision/hard013.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, a provincial industry and information department sought to evaluate the government subsidy utilization efficiency of enterprises of different sizes in the rubber and plastic products industry. After dividing enterprises into three groups by total assets—large (top 1/3 rounded up), medium (middle 1/3 rounded up), and small (bottom 1/3)—which enterprise size group has the highest subsidy utilization efficiency? What is its efficiency value?

Output guidelines:
The answer should be a numeric value with 2 decimal places. Output only the number, without units or text. If relevant data cannot be found, please answer "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:
`["Large", 166.33]`

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.