Triple

T5949207
Position Surface form Disambiguated ID Type / Status
Subject Laura Prepon E132354 entity
Predicate spouse P13 FINISHED
Object Ben Foster E126305 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: Ben Foster | Statement: [Laura Prepon, spouse, Ben Foster]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ben Foster
Context triple: [Laura Prepon, spouse, Ben Foster]
  • A. Ben Foster chosen
    Ben Foster is an American actor known for his intense, often gritty performances in films such as "3:10 to Yuma," "Hell or High Water," and "The Messenger."
  • B. Ben Foster
    Ben Foster is a British composer and orchestrator best known for his work on television scores, including contributions to the revived Doctor Who series.
  • C. James Badge Dale
    James Badge Dale is an American actor known for his intense character roles in film and television, including performances in projects like "The Pacific," "Iron Man 3," and "World War Z."
  • D. Joel David Moore
    Joel David Moore is an American actor and director best known for his roles in films like "Avatar" and the TV series "Bones."
  • E. Shea Whigham
    Shea Whigham is an American character actor known for his intense, often gritty supporting roles in film and television, including prominent parts in series like "Boardwalk Empire" and numerous acclaimed movies.
  • 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_69c00869d3308190af89b2453e0f7546 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c0397deea08190b9397d0413740300 completed March 22, 2026, 6:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0e3c4120c8190bab97f91a7bc7030 completed March 23, 2026, 6:55 a.m.
Created at: March 22, 2026, 4:02 p.m.