I need to analyze the temporal trend in this boxing champions dataset. From the evidence, I can see that `Begin_reign` contains dates when championship reigns started. Let me examine the distribution of championship reigns over time more carefully. The previous queries attempted to extract years from `Begin_reign`, but the results show some malformed entries (`,071`, `,226`, `,522`, `,611`) alongside proper years. Let me query the data more robustly to understand the temporal pattern. ```json { "action": "tool_call", "reasoning": "I need to properly extract and analyze the temporal distribution of championship reigns. The Begin_reign column contains dates, but some entries appear malformed. I'll query to get a clean view of reigns by decade or year range, filtering out invalid entries, to identify the overall trend.", "tool": "query_sql", "args": { "sql": "SELECT CASE WHEN Begin_reign LIKE '%188%' THEN '1880s' WHEN Begin_reign LIKE '%189%' THEN '1890s' WHEN Begin_reign LIKE '%190%' THEN '1900s' WHEN Begin_reign LIKE '%191%' THEN '1910s' WHEN Begin_reign LIKE '%192%' THEN '1920s' WHEN Begin_reign LIKE '%193%' THEN '1930s' WHEN Begin_reign LIKE '%194%' THEN '1940s' WHEN Begin_reign LIKE '%195%' THEN '1950s' WHEN Begin_reign LIKE '%196%' THEN '1960s' WHEN Begin_reign LIKE '%197%' THEN '1970s' WHEN Begin_reign LIKE '%198%' THEN '1980s' WHEN Begin_reign LIKE '%199%' THEN '1990s' WHEN Begin_reign LIKE '%200%' THEN '2000s' WHEN Begin_reign LIKE '%201%' THEN '2010s' ELSE 'Other' END AS decade, COUNT(*) AS reign_count FROM raw.championsdataset WHERE Begin_reign NOT LIKE '%,%' GROUP BY decade ORDER BY decade", "purpose": "Count championship reigns by decade to identify the temporal trend" } } ```