```json { "table_name": "nia_message_llm_calls", "primary_key": "id", "candidates": [ { "rank": 0, "partition_key": "message_id", "partition_key_label": "E", "partition_key_reasoning": "Entity column identified by classifier. RPUV=7.34 is ideal for partitioning (average ~7 LLM calls per message). Top 3 coverage 1.07% indicates good distribution. Semantically represents the conversation/message whose LLM calls are being tracked.", "clustering_key": "id", "clustering_key_label": "S", "clustering_key_reasoning": "Sequence column identified by classifier. Monotonicity=1.000 confirms strict ascending order. As a sequential ID, it orders LLM calls chronologically within each message's timeline.", "world_line": "message_id", "session_column": null, "confidence": "HIGH", "notes": "This is an event log of LLM API calls made during message processing. Each message_id represents a conversation/message entity that triggers multiple LLM calls (agent_decide, agent_repair, agent_synthesize). The id column provides strict sequential ordering of these calls. Partitioning by message_id groups all LLM calls for a given message together, enabling efficient analysis of per-message LLM usage patterns, costs, and latency." } ] } ```