Triple

T31076066
Position Surface form Disambiguated ID Type / Status
Subject Jonathan Hart E791963 entity
Predicate hasRelationshipTypeWithJenniferHart P202936 FINISHED
Object romantic partnership LITERAL FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: romantic partnership | Statement: [Jonathan Hart, hasRelationshipTypeWithJenniferHart, romantic partnership]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasRelationshipTypeWithJenniferHart
Context triple: [Jonathan Hart, hasRelationshipTypeWithJenniferHart, romantic partnership]
  • A. relationshipTypeWithJohnHartigan
    Indicates the specific nature or category of relationship that an entity has with John Hartigan.
  • B. hasRelationshipTypeWith Alexandra Bergson
    Indicates that there exists a specific type or category of relationship between an entity and Alexandra Bergson.
  • C. hasRelationshipTypeWith Owen Hunt
    Indicates that there exists a specific type of interpersonal or relational connection between an entity and Owen Hunt.
  • D. hasRelationshipTypeWith Tai Frasier
    Indicates that there exists a specific type of relationship between an entity and Tai Frasier.
  • E. hasRelationshipTypeWithRoryGilmore
    Indicates that an entity has a specific type of interpersonal relationship or connection with Rory Gilmore.
  • F. None of above. chosen

Provenance (4 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69f224ccdbbc81909b0cdb4cc2d70c7a completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_6a00d24043e8819090cc473b6c0923d0 completed May 10, 2026, 6:45 p.m.
PD Predicate disambiguation batch_6a00d1ec12fc81908c514ed088ef8300 completed May 10, 2026, 6:43 p.m.
PDg Predicate description generation batch_6a00d23f402c8190bf49c0d4cce10b73 completed May 10, 2026, 6:45 p.m.
Created at: April 29, 2026, 9:02 p.m.