Law as a chain of events

The problem is not purely textual

A document describes an event, but it is not the event itself. A filing, decision, payment, and service notice may concern one matter while carrying different dates and procedural effects. Flat passage retrieval can easily merge those layers.

A Legal AI result should identify not only matching text, but also the document's role, participant, time, and relationship to other actions.

Working hypothesis

Analysis becomes more controllable when the corpus is represented as a graph of events and evidence. Nodes stand for actions, documents, parties, and qualifications; edges preserve explicit temporal and procedural relationships.

A minimal model

1. Register the source, version, and scope of each document.
2. Separate a mention of an event from an established fact.
3. Connect participant, time, and procedural role.
4. Record the rule that links reconstruction to qualification.
5. Preserve contradictions and missing data.

How to test it

A benchmark should test reconstruction of the path, not similarity of one passage. Each scenario includes expected events, sources, relationships, and a failure the system must not accept.

The most important negative case is a convincing chronology assembled from documents concerning the wrong person or the wrong period of applicable law.

Practical conclusion

The ontology is a contract between data, retrieval, and interpretation. If that contract cannot be shown and tested, the final answer remains hard to audit regardless of model quality.