tasks/task_015_comprehensive_decision_hard_hard009.md

metadata

id: task_015_comprehensive_decision_hard_hard009
name: comprehensive_decision-hard-hard009
category: comprehensive_decision
grading_type: llm_judge
timeout_seconds: 1200
gold_file: qa_gold/comprehensive_decision/hard009.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, an investment manager at a merger and acquisition fund was seeking "high R&D, low valuation" M&A targets in the textile, footwear and apparel industry, but the scope was limited to provinces covered by textile, footwear and apparel industry-related policies. The prerequisite for screening valid enterprises is: net profit amount strictly greater than zero, and both R&D investment ratio and company market cap fields have data records. On this basis, first use all valid enterprises in the industry as the benchmark population to calculate the median R&D investment ratio and the median P/E ratio respectively; then from the subset of valid enterprises located in policy-covered provinces, filter enterprises whose R&D investment ratio is higher than the industry median and whose P/E ratio is lower than the industry median. How many enterprises satisfy the above dual screening conditions? (P/E ratio = company market cap (100 million yuan) ÷ net profit amount (100 million yuan))

Output guidelines: The answer should be an integer. 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: Single-answer Correctness (Weight: 100%)

Gold answer JSON: 9

Scoring rules:

• Judge semantic equivalence between the model final answer and the gold answer.
• Return scores with one key match as 1 or 0.
• Return total as 1.0 if equivalent, otherwise 0.0.