Triple

T7354721
Position Surface form Disambiguated ID Type / Status
Subject Chapter Two E169593 entity
Predicate producer P490 FINISHED
Object Lawrence Weingarten E30855 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: Lawrence Weingarten | Statement: [Chapter Two, producer, Lawrence Weingarten]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lawrence Weingarten
Context triple: [Chapter Two, producer, Lawrence Weingarten]
  • A. Lawrence Weingarten chosen
    Lawrence Weingarten was an American film producer best known for his work at MGM during Hollywood’s classic studio era, overseeing numerous popular comedies and dramas.
  • B. Jeffrey A. Rosen
    Jeffrey A. Rosen is an American lawyer and government official who has served in senior roles at the U.S. Department of Transportation and later as Deputy Attorney General of the United States.
  • C. Kenneth Posner
    Kenneth Posner is a prominent American theatrical lighting designer known for his work on numerous Broadway productions.
  • D. Allen Rivkin
    Allen Rivkin was an American screenwriter active during Hollywood’s studio era, known for his work on numerous mid-20th-century films.
  • E. Stephen E. Rivkin
    Stephen E. Rivkin is an American film editor best known for his work on major feature films including the "Avatar" series.
  • 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_69c68a59f2288190877ca15c19b1e822 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f10e71fc81909307ca39a61142d3 completed March 27, 2026, 9:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69c856a22b208190821bcbf21a074bb8 completed March 28, 2026, 10:30 p.m.
Created at: March 27, 2026, 3:05 p.m.