Triple

T6654827
Position Surface form Disambiguated ID Type / Status
Subject Trophy Wife E150913 entity
Predicate executiveProducer P7225 FINISHED
Object Aaron Kaplan E567698 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: Aaron Kaplan | Statement: [Trophy Wife, executiveProducer, Aaron Kaplan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aaron Kaplan
Context triple: [Trophy Wife, executiveProducer, Aaron Kaplan]
  • A. Aaron Kaplan chosen
    Aaron Kaplan is a television producer and executive known for developing and overseeing numerous network and cable series through his production company.
  • B. Greg Kaplan
    Greg Kaplan is an economist known for his research on household heterogeneity, consumption, and macroeconomic policy, and for his contributions to modern macroeconomic modeling.
  • C. Jonathan Kaplan
    Jonathan Kaplan is an American film and television director best known for his work on the acclaimed 1988 courtroom drama "The Accused."
  • D. Larry Kaplan
    Larry Kaplan is a pioneering video game designer and programmer best known as one of the co-founders of Activision and an early developer for the Atari 2600.
  • E. Sol Kaplan
    Sol Kaplan was an American composer best known for his film and television scores, including work in mid-20th-century Hollywood.
  • 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_69c687f2c9508190a60b9aad31d3f358 completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6b049958c8190bb3ef0d4c281825b completed March 27, 2026, 4:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8569b932481909d41130303e21518 completed March 28, 2026, 10:30 p.m.
Created at: March 27, 2026, 2:01 p.m.