Triple

T31709179
Position Surface form Disambiguated ID Type / Status
Subject Marcus Graham E809267 entity
Predicate hasRomanticRelationshipsInWork P93858 FINISHED
Object Angela Lewis NE NERFINISHED

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: Angela Lewis | Statement: [Marcus Graham, hasRomanticRelationshipsInWork, Angela Lewis]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasRomanticRelationshipsInWork
Context triple: [Marcus Graham, hasRomanticRelationshipsInWork, Angela Lewis]
  • A. hasLoveInterestInWork chosen
    Indicates that one entity is portrayed as a romantic love interest of another entity within a specific creative work.
  • B. hasRelationshipToWork
    Indicates a relationship where an entity has a specific connection, role, or association with a particular work or piece of work.
  • C. hasFamilyRelationInWork
    Indicates that there exists a family relationship between two entities within the context of a specific work (e.g., book, film, or other creative work).
  • D. worksInCloseRelationshipWith
    Indicates a collaborative professional relationship in which two or more entities work together closely and interact frequently to achieve shared goals.
  • E. spouseWorkWith
    Indicates that a person’s spouse works together with a specified person, typically as colleagues in the same workplace or professional context.
  • F. None of above.

Provenance (3 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_69f348df4e048190a4a5a9932ada78d6 completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69fd4f39b5008190b83b3227ce22c509 completed May 8, 2026, 2:49 a.m.
PD Predicate disambiguation batch_69fd4df17c548190a4e2a6fea70f7e10 completed May 8, 2026, 2:44 a.m.
Created at: April 30, 2026, 11:15 p.m.