# graphify

[](https://github.com/rhanka/graphify/actions/workflows/typescript-ci.yml)

**graphify turns a corpus into a reconciled, ontology-typed knowledge graph.** Most knowledge isn't documentary — it doesn't live as one fact in one file. It's *entities and relations scattered across sources*: the same person under three names in twenty-five books, a component named one way in a CSV registry and another way in a manual, a case that only makes sense once its evidence, motive, and method are linked. Prose and docs flatten that structure; a knowledge graph keeps it. graphify extracts canonical entities and typed relations, deduplicates and reconciles them across sources under a configurable ontology, and gives you back a queryable graph your assistant — or you, from the terminal — can reason over.


*The flagship corpus: **1,193 canonical entities across 19 ontology types** (Work, Saga, Case, Character, Evidence, Motive, ForensicMethod, DisguisePersona, Alias…) reconciled from **25 public-domain mystery works**, clustered into 99 communities — here with the entity panel open on Sherlock Holmes.* **Explore the live studio → https://mystery-saga.sent-tech.ca/studio/**

## Why a knowledge graph, not more prose

Four things a graph gives you that documents can't:

- **Queryable structure** — ask `graphify query "what connects Irene Adler to the Bohemia case?"` and get a path through typed nodes and edges, not a wall of search hits to re-read. - **Entity reconciliation** — "Holmes", "Mr. Sherlock Holmes", and a disguised persona collapse into one canonical entity with aliases and evidence refs, instead of staying three scattered mentions. - **Cross-s