tasks/task_012_comprehensive_decision_hard_hard006.md

metadata

id: task_012_comprehensive_decision_hard_hard006
name: comprehensive_decision-hard-hard006
category: comprehensive_decision
grading_type: llm_judge
timeout_seconds: 1200
gold_file: qa_gold/comprehensive_decision/hard006.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, to measure the impact of different ownership backgrounds on the operating performance of specialized equipment manufacturing enterprises, an analysis team compared each enterprise's return on equity (ROE) level with the industry-wide return level in its province to calculate "excess ROE" as a relative performance indicator. Specifically: enterprise ROE is calculated as net profit divided by net assets (total assets minus total liabilities) multiplied by 100%; provincial industry benchmark ROE is extracted from provincial industry summary tables, calculated as total industry net profit divided by total industry net assets (total assets minus total liabilities) multiplied by 100%; each enterprise's excess ROE is the difference between its own ROE and its province's benchmark ROE. After grouping by ownership type, which ownership category has the highest mean excess ROE among enterprises? What is that mean value in percentage points?

Output guidelines: The answer should be a percentage value with 2 decimal places. Output only the number, without the % symbol 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: ["Collective Enterprise", 5.17]

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.