Triple

T5227146
Position Surface form Disambiguated ID Type / Status
Subject Fifty Million Frenchmen E118017 entity
Predicate hasNotableCreator P4321 FINISHED
Object Herbert Fields E505783 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: Herbert Fields | Statement: [Fifty Million Frenchmen, hasNotableCreator, Herbert Fields]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Herbert Fields
Context triple: [Fifty Million Frenchmen, hasNotableCreator, Herbert Fields]
  • A. Herbert Fields chosen
    Herbert Fields was an American librettist and playwright best known for writing the books for numerous successful Broadway musicals in the early to mid-20th century.
  • B. Joseph Fields
    Joseph Fields was an American playwright, screenwriter, and producer known for his work on Broadway and in Hollywood during the mid-20th century.
  • C. Elwood Bredell
    Elwood Bredell was an American cinematographer best known for his work on classic Hollywood films and film noir in the 1930s and 1940s.
  • D. Charles Schoenbaum
    Charles Schoenbaum was an American cinematographer known for his work on numerous Hollywood films during the mid-20th century.
  • E. Charles K. Feldman
    Charles K. Feldman was a prominent American film producer and talent agent known for shaping major Hollywood projects and representing high-profile stars during the mid-20th century.
  • 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_69bd4466fb8c819083b806a79414d7e4 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd847049648190ab24693e92f0dad1 completed March 20, 2026, 5:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf06b46eac81908b985363733fcd63 completed March 21, 2026, 8:59 p.m.
Created at: March 20, 2026, 1:48 p.m.