Datasets:

GTML-LAB
/

DataClaw

id: task_029_comprehensive_decision_medium_medium004
name: comprehensive_decision-medium-medium004
category: comprehensive_decision
grading_type: llm_judge
timeout_seconds: 1200
gold_file: qa_gold/comprehensive_decision/medium004.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, rank provinces by profitability in chemical raw materials and chemical products manufacturing. Provincial operating profit margin is computed as total operating profit divided by total operating revenue. Using this as the ranking criterion, what is Guangdong Province's rank? Apply the same ranking to all relevant enterprises within Guangdong Province—which enterprise ranks first?

Output guidelines: Two answers required: first is Guangdong Province's rank in the provincial ranking (integer, e.g. "6" means 6th place); second is the full name of the top-ranked enterprise in Guangdong, which must match the "Company Name" field in company_profile.csv. 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: [6, "Hengyi Changhua Technology Co., Ltd."]

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.