Triple

T17739829
Position Surface form Disambiguated ID Type / Status
Subject John Agar E442822 entity
Predicate name P16 FINISHED
Object John Agar 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: John Agar | Statement: [John Agar, name, John Agar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Agar
Context triple: [John Agar, name, John Agar]
  • A. John Agar chosen
    John Agar was an American film actor best known for his roles in Westerns and 1950s science fiction movies, and for his early marriage to Shirley Temple.
  • B. Monty Beragon
    Monty Beragon is a charming but irresponsible playboy whose relationship with Mildred Pierce contributes significantly to her personal and financial downfall in James M. Cain’s novel.
  • C. Troy Donahue
    Troy Donahue was an American actor and teen idol of the late 1950s and early 1960s, best known for his roles in romantic dramas and beach-themed films.
  • D. Hal Linden
    Hal Linden is an American actor, television director, and musician best known for his Emmy-winning role as the title character in the sitcom "Barney Miller."
  • E. James Darren
    James Darren is an American actor, singer, and director best known for his teen idol status in the late 1950s and 1960s, with notable roles in films like "Gidget" and the TV series "The Time Tunnel."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9ed3a2081909b2ec0d4dd2f4c37 completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e47acc2610819099451b02bb51891f completed April 19, 2026, 6:48 a.m.
Created at: April 10, 2026, 10:09 a.m.