Learning Paths

Learning paths sequence recipes, canonical examples, term pages, and external adoption targets for common implementation goals.

Path Audience Goal
Model a Person Without Flattening Identity Developers importing contact, profile, or biography data. Start with a person, add names and contact points, then keep authority links and display choices separate from identity.
Model a Contested or Attributed Claim Developers handling evidence, provenance, or disagreement. Represent claims by vantage and evidence rather than overwriting facts into a single global truth slot.
Publish Web Structured Data Developers projecting native GMEOW into web-facing JSON-LD. Model documents, events, people, and organizations natively, then inspect which fields project cleanly to broad consumers.
Ship Offline GTS Documentation Developers distributing GMEOW snapshots or local docs. Treat the distribution file, embedded docs, profiles, segments, codecs, and lineage as first-class graph facts.
Audit AI and Graph-RAG Pipelines Developers recording extracted facts, chunks, and tools. Connect generated claims to source chunks, evidence spans, tools, model context, and provenance before consumers see them.

Model a Person Without Flattening Identity

Audience: Developers importing contact, profile, or biography data.

Start with a person, add names and contact points, then keep authority links and display choices separate from identity.

Steps

  1. Read Model Person Names Without a Preferred-Name Slot.
  2. Copy and adapt Agent Sortals, Person Names, Contact Points, Authority Links.
  3. Inspect gmeow:Person, gmeow:PersonName, gmeow:NameUsage, gmeow:ContactPoint, gmeow:displayable.
  4. Check adoption targets schema, foaf, vcard, wikidata.

Model a Contested or Attributed Claim

Audience: Developers handling evidence, provenance, or disagreement.

Represent claims by vantage and evidence rather than overwriting facts into a single global truth slot.

Steps

  1. Read Model Contested or Attributed Facts.
  2. Copy and adapt Contested Authorship, Notability Assessment, Import Lineage, Software Release.
  3. Inspect gmeow:StandpointClaim, gmeow:Attestation, gmeow:accordingTo, gmeow:confidence.
  4. Check adoption targets prov, crminf, wikidata.

Publish Web Structured Data

Audience: Developers projecting native GMEOW into web-facing JSON-LD.

Model documents, events, people, and organizations natively, then inspect which fields project cleanly to broad consumers.

Steps

  1. Read Publish Documents for Schema.org Consumers, Model Events and Participants.
  2. Copy and adapt Web Presence, Wedding, Post And Membership, Located Place.
  3. Inspect gmeow:Document, gmeow:Event, gmeow:Participation, gmeow:Organization, gmeow:Place.
  4. Check adoption targets schema, prov, geo, org.

Ship Offline GTS Documentation

Audience: Developers distributing GMEOW snapshots or local docs.

Treat the distribution file, embedded docs, profiles, segments, codecs, and lineage as first-class graph facts.

Steps

  1. Read Describe Offline GTS Distribution.
  2. Copy and adapt Dist Package, Import Lineage, Licensed Dataset.
  3. Inspect gmeow:GTSDocument, gmeow:GTSProfile, gmeow:GTSSegment, gmeow:usesTransformCodec.
  4. Check adoption targets dcat, void, spdx.

Audit AI and Graph-RAG Pipelines

Audience: Developers recording extracted facts, chunks, and tools.

Connect generated claims to source chunks, evidence spans, tools, model context, and provenance before consumers see them.

Steps

  1. Read Model Graph-RAG Dataset Lineage.
  2. Copy and adapt Grounded Claim, Lillith Dataset, Lillith Pipeline, Agent Trajectory.
  3. Inspect gmeow:Dataset, gmeow:Chunk, gmeow:ExtractedEntity, gmeow:EvidenceSpan, gmeow:usedModel.
  4. Check adoption targets prov, dcat, schema.