{
    "action": "final",
    "content": "Distinct counts per categorical column, by monthly table, are in the rendered result (query step-2). Summary:\n\n- **Company Name**: 114 distinct in January (115 rows), 96 in February (96 rows), 98 in March (98 rows), 94 in April (95 rows), and only 24 in May (42 rows).\n- **Location**: 11 (Jan), 13 (Feb), 17 (Mar), 39 (Apr), 16 (May).\n- **Sector**: 42 (Jan), 39 (Feb), 54 (Mar), 58 (Apr), 19 (May).\n- **Stage**: 12 (Jan), 11 (Feb), 11 (Mar), 18 (Apr), 6 (May).\n- **Investor**: 107 (Jan), 86 (Feb), 90 (Mar), 54 (Apr), 22 (May).\n\n**Caveats**\n- The data is split across five separate tables with no join key, so counts are per month and cannot be summed into a global distinct count, because the same sector, location, or investor can appear in several months.\n- May has 42 rows but only 24 distinct company names. The card notes that some May rows are misaligned or blank, so its low distinct counts probably reflect blank or NULL values rather than real low diversity. COUNT(DISTINCT) ignores NULLs.\n- April's 39 distinct locations is much higher than the other months (11–17). That may be genuine, or it may reflect inconsistent location formatting. I did not check the values.\n- Free-text columns (What it does, Founders) were not counted as categorical."
}