Triple

T7354048
Position Surface form Disambiguated ID Type / Status
Subject The Girl Who Had Everything E169577 entity
Predicate producer P490 FINISHED
Object Armand Deutsch E583716 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: Armand Deutsch | Statement: [The Girl Who Had Everything, producer, Armand Deutsch]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Armand Deutsch
Context triple: [The Girl Who Had Everything, producer, Armand Deutsch]
  • A. Armand Deutsch chosen
    Armand Deutsch was an American film producer active during Hollywood's mid-20th century studio era.
  • B. Armand Schaefer
    Armand Schaefer was an American film director and producer known for his work on adventure serials and B-movies during the early 20th century.
  • C. Felix Weil
    Felix Weil was a German Marxist intellectual and wealthy benefactor best known for financing and helping to establish the Frankfurt School’s Institute for Social Research.
  • D. Marcel Bloch
    Marcel Bloch, better known as Marcel Dassault, was a prominent French aircraft industrialist and founder of the Dassault aviation and defense empire.
  • E. Adolph Deutsch
    Adolph Deutsch was a British-American composer and conductor best known for his film scores during Hollywood's Golden Age, including classic noir and crime dramas.
  • 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_69c6f10d3ef88190b3a0763d80b1e726 completed March 27, 2026, 9:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69c87067b98081908439af85623a97ea completed March 29, 2026, 12:20 a.m.
Created at: March 27, 2026, 3:05 p.m.