Triple

T13011873
Position Surface form Disambiguated ID Type / Status
Subject Melissa Garner Wylie E322435 entity
Predicate notableRelative P367 FINISHED
Object Jennifer Garner E36244 NE 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: Jennifer Garner | Statement: [Melissa Garner Wylie, notableRelative, Jennifer Garner]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jennifer Garner
Context triple: [Melissa Garner Wylie, notableRelative, Jennifer Garner]
  • A. Jennifer Garner chosen
    Jennifer Garner is an American actress known for her starring role in the television series "Alias" and for her performances in films such as "13 Going on 30" and "Dallas Buyers Club."
  • B. Jessica Alba
    Jessica Alba is an American actress and businesswoman known for her roles in films like "Fantastic Four" and for founding the consumer goods company The Honest Company.
  • C. Kate Bosworth
    Kate Bosworth is an American actress best known for her roles in films such as "Blue Crush" and "Superman Returns."
  • D. Katherine Heigl
    Katherine Heigl is an American actress and former fashion model best known for her roles in the television series "Grey's Anatomy" and various romantic comedy films.
  • E. Rachel McAdams
    Rachel McAdams is a Canadian actress known for her versatile performances in films such as "Mean Girls," "The Notebook," and "Spotlight."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69d807657e8c8190bd9435ee2f823845 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97e9e14b88190a2cee8e0c9bf31c8 completed April 10, 2026, 10:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6cbc77f308190b3b47f7a092db434 completed May 3, 2026, 4:15 a.m.
Created at: April 9, 2026, 8:49 p.m.