Triple

T10818613
Position Surface form Disambiguated ID Type / Status
Subject The Accused E255298 entity
Predicate director P255 FINISHED
Object Jonathan Kaplan E255298 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: Jonathan Kaplan | Statement: [The Accused, director, Jonathan Kaplan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jonathan Kaplan
Context triple: [The Accused, director, Jonathan Kaplan]
  • A. Jonathan Kaplan chosen
    Jonathan Kaplan is an American film and television director best known for his work on the acclaimed 1988 courtroom drama "The Accused."
  • 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. Aaron Kaplan
    Aaron Kaplan is a television producer and executive known for developing and overseeing numerous network and cable series through his production company.
  • 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_69d6aa8081448190a9324184f2bd1c26 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7344866f88190be4addb7c8020fce completed April 9, 2026, 5:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69f12f646ec88190ab4745c52798b599 completed April 28, 2026, 10:06 p.m.
Created at: April 8, 2026, 9:18 p.m.