```json { "action": "tool_call", "reasoning": "I've already identified several data quality issues from the evidence: (1) 14,661 rows in ittf_rankings and 11,224 in ittf_rankings_women have Rank=0 but non-zero Points, (2) gender mismatches (14 blank, 6 'W' in men's table; 1 blank, 21 'M' in women's table), (3) 47,662 duplicate rank periods in men's and 30,885 in women's. Now I need to check for temporal inconsistencies and gaps. The step-5 result shows monthly snapshots but I notice it jumps from 2005 month 2 to month 4 — let me check for systematic gaps in the temporal coverage.", "tool": "query_sql", "args": { "sql": "WITH all_periods AS (SELECT DISTINCT YearNum, MonthNum FROM raw.ittf_rankings UNION SELECT DISTINCT YearNum, MonthNum FROM raw.ittf_rankings_women), expected AS (SELECT y.year AS YearNum, m.month AS MonthNum FROM (SELECT DISTINCT YearNum AS year FROM all_periods) y CROSS JOIN (SELECT DISTINCT MonthNum AS month FROM all_periods WHERE MonthNum > 0) m), missing AS (SELECT e.YearNum, e.MonthNum FROM expected e LEFT JOIN all_periods a ON e.YearNum = a.YearNum AND e.MonthNum = a.MonthNum WHERE a.YearNum IS NULL) SELECT YearNum, MonthNum FROM missing ORDER BY YearNum, MonthNum LIMIT 50", "purpose": "Identify missing year-month combinations to detect temporal gaps in the rankings data", "source": "raw" } } ```