LLMs Meet Logic: SymbolicThought Turns AI Relationship Guesswork into Graphs
TL;DR for operators SymbolicThought1 is a useful reminder that relationship extraction is not a vibes problem. It is a graph problem wearing a language-model costume. The paper proposes a human-in-the-loop system for extracting character relationships from narrative text. The pipeline lets an LLM propose characters and relations, then applies symbolic rules to infer missing edges, detect contradictions, retrieve supporting evidence, and ask humans to confirm or correct what matters. That is the important mechanism: the LLM is not trusted as a final judge. It is treated as a noisy extractor inside a controlled annotation workflow. ...