How can a dataset expose the reasoning behind a correction?

A generated release can separate correction intent from the events that implement it. An `adjustment_intent` record could contain `intent_id`, `target_record_id`, `reason_code`, `requested_outcome` and `evidence_refs`. An associated event stream could contain `event_id`, `intent_id`, `event_type`, `actor_role` and `recorded_at`.

This structure answers two different questions: what the business wanted to correct, and what actually happened. One intent may produce several events, including a returned request, revised proposal and eventual posting. Flattening them into one row would erase that sequence.

JSONL can carry ordered events for sequence tasks, while Parquet can expose intent-level tables for analytical inspection. The schema should state whether order is determined by an explicit sequence number or a timestamp, especially where events share the same recorded time.

For a reseller, preserve the intent-to-event links when converting formats or creating customer subsets. A useful ingestion check confirms that every included execution event resolves to an included intent. This allows customers to ask whether an action fulfilled the stated request, rather than merely whether an edit occurred.