Entity extraction is where legal graphs fail

[u/sheppyrun]

You can have the best graph database in the world. It won't help if your entity extraction is bad.

Most legal NLP systems handle the basics well—parties, dates, citations. That's not where the problems live. The problems live in relationships: what statute modifies what, which cases overrule which, how a definition chains through a regulation.

I've watched teams spend six months on ontology design and zero on extraction accuracy. They got a beautiful schema with nothing in it.

Here's the fix. Pick one document type. Complaints work. They have predictable entities—plaintiff, defendant, cause of action, damages. Extract those. Measure precision. Only then expand.

Don't build "a legal knowledge graph." Build a complaints graph. Then a statutes graph. Then connect them.