Triple

T6161664
Position Surface form Disambiguated ID Type / Status
Subject Dallas Buyers Club E137456 entity
Predicate starring P1507 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: [Dallas Buyers Club, starring, Jennifer Garner]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jennifer Garner
Context triple: [Dallas Buyers Club, starring, 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. Rachel McAdams
    Rachel McAdams is a Canadian actress known for her versatile performances in films such as "Mean Girls," "The Notebook," and "Spotlight."
  • E. May McAvoy
    May McAvoy was a prominent American silent film actress best known for her leading roles in the 1920s and her appearance in early sound cinema.
  • 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_69c008a54fc88190b6ce4416490ca79d completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c05d371484819090c18b62b095b49e completed March 22, 2026, 9:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69c14194d31081908e61a867f11117b4 completed March 23, 2026, 1:35 p.m.
Created at: March 22, 2026, 4:17 p.m.