Turn prose into a structured knowledge graph
A wall of text hides the relationships inside it. This tool uses your own API key to extract those relationships as subject-predicate-object triples — the building blocks of a knowledge graph — and lists every unique entity, so you can see at a glance who is connected to what and how.
How it works
You paste text and, optionally, the entity types you care about. The tool sends a
structured extraction prompt that asks the model to return only a JSON array of
{ subject, predicate, object } triples, with consistent entity naming. It then
parses that JSON robustly — tolerating code fences and alternate key names like
relation or from/to — and renders the triples as a table alongside a count of
unique entities. One click exports the whole graph as Graphviz DOT, ready to render
in any DOT-compatible viewer.
Tips and notes
Naming the entity types you want focuses the model and cuts noise — for example, “people, organizations, products” keeps it from extracting trivial relations. Review the unique-entity list for near-duplicates the model may have introduced despite the consistency instruction, and merge them in your downstream graph if needed. For long documents, extract in sections and combine the triples. Your key never leaves your browser except to call the provider directly, and it is never stored.