Triple

T7567304
Position Surface form Disambiguated ID Type / Status
Subject Walter Wanger E178949 entity
Predicate name P16 FINISHED
Object Walter Wanger E178949 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: Walter Wanger | Statement: [Walter Wanger, name, Walter Wanger]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Walter Wanger
Context triple: [Walter Wanger, name, Walter Wanger]
  • A. Walter Wanger chosen
    Walter Wanger was an American film producer known for his work on numerous influential Hollywood films from the 1930s through the 1950s, often tackling socially conscious and ambitious subjects.
  • B. Joseph Schenck
    Joseph Schenck was a pioneering early Hollywood film executive and producer who played a key role in shaping the American movie industry.
  • C. Dean Zanuck
    Dean Zanuck is an American film producer and member of the prominent Zanuck filmmaking family, known for working on major Hollywood projects.
  • D. Jesse L. Lasky Jr.
    Jesse L. Lasky Jr. was an American screenwriter known for his work on major Hollywood epics and adventure films in the mid-20th century.
  • E. Jesse L. Lasky
    Jesse L. Lasky was a pioneering American film producer and co-founder of Paramount Pictures who played a key role in the development of early Hollywood cinema.
  • 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_69c69f2f80288190b95cceb4da92ab2b completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f91c717881909c88901cc3b101c7 completed March 27, 2026, 9:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8616207f88190834dc2412001af3f completed March 28, 2026, 11:16 p.m.
Created at: March 27, 2026, 3:51 p.m.