Looking at the evidence I've already gathered, I have the distinct counts for all categorical columns in the dataset. Let me compile this into a comprehensive answer. From the inspect_columns results and the SQL queries I ran, here are the distinct value counts for each categorical column: ## Categorical Column Distinct Value Counts **Identity & Status Columns:** - Name: 293 distinct values - Status: 1 distinct value - Certainty: 2 distinct values - Single cluster?: 1 distinct value **Hardware & Technology:** - Chip type (primary): 36 distinct values - Chip type (secondary): 11 distinct values - GPU Supplier (primary): 10 distinct values - GPU supplier (secondary): 4 distinct values - Hardware note: 40 distinct values **Geographic & Organizational:** - Country: 37 distinct values - Owner: 131 distinct values - Location: 149 distinct values - Users: 109 distinct values - Sector: 4 distinct values **Temporal & Notes:** - First Operational Date Note: 184 distinct values - Certainty Note: 43 distinct values **Relationships & Flags:** - Builds Upon: 30 distinct values - Superseded by: 30 distinct values - Possible Duplicate: 1 distinct value - Possible Duplicate Of: 1 distinct value - Largest existing cluster when first operational: 17 distinct values - Include in Standard Analysis: 1 distinct value - Exclude: 1 distinct value - Noteworthy: 1 distinct value - Decommissioned Date (if applicable): 1 distinct value The dataset shows high cardinality in organizational identifiers (Owner: 131, Location: 149, Users: 109) and moderate diversity in hardware specifications (Chip type primary: 36, GPU Supplier primary: 10), while several flag columns have only 1 distinct value, indicating they may be constant across all records.