I need to analyze the temporal trend in this boxing champions dataset. From the evidence, I can see that `Begin_reign` is a temporal column, but the data has quality issues (some values start with commas like ",071", ",226", etc.). Let me extract clean year data and analyze the trend properly. ```json { "action": "tool_call", "reasoning": "The previous queries had issues with data quality in Begin_reign. I need to extract years properly, handling the malformed entries, and count championship reigns per year to see the overall trend. I'll use a more robust approach to extract 4-digit years from the Begin_reign column.", "tool": "query_sql", "args": { "sql": "WITH cleaned_years AS (SELECT CASE WHEN Begin_reign LIKE '%,%' THEN CAST(REGEXP_EXTRACT(Begin_reign, '([0-9]{4})', 1) AS INTEGER) WHEN Begin_reign LIKE '% %' THEN CAST(REGEXP_EXTRACT(Begin_reign, '([0-9]{4})', 1) AS INTEGER) ELSE NULL END as year FROM raw.championsdataset WHERE Begin_reign IS NOT NULL AND REGEXP_EXTRACT(Begin_reign, '([0-9]{4})', 1) IS NOT NULL) SELECT year, COUNT(*) as reign_count FROM cleaned_years WHERE year IS NOT NULL GROUP BY year ORDER BY year", "purpose": "Extract clean 4-digit years from Begin_reign and count reigns per year to analyze temporal trend" } } ```