Triple

T2008241
Position Surface form Disambiguated ID Type / Status
Subject Yolanda and the Thief E43633 entity
Predicate screenwriter P2831 FINISHED
Object Irving Brecher E244601 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: Irving Brecher | Statement: [Yolanda and the Thief, screenwriter, Irving Brecher]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Irving Brecher
Context triple: [Yolanda and the Thief, screenwriter, Irving Brecher]
  • A. Irving Brecher chosen
    Irving Brecher was an American screenwriter best known for his work on Marx Brothers comedies and classic Hollywood films of the 1930s and 1940s.
  • B. Benjamin Rapoport
    Benjamin Rapoport is a neurosurgeon and entrepreneur best known as one of the co-founders of the brain–computer interface company Neuralink.
  • C. Edmund Meisel
    Edmund Meisel was a German composer best known for his pioneering, politically charged film scores for silent cinema, particularly his work on Soviet montage films.
  • D. Charles Schoenbaum
    Charles Schoenbaum was an American cinematographer known for his work on numerous Hollywood films during the mid-20th century.
  • E. Pandro S. Berman
    Pandro S. Berman was a prominent American film producer of Hollywood’s classic era, known for overseeing numerous successful MGM and RKO pictures.
  • 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_69a88716e9f08190946313fdc949e3cf completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb89aca908190b8b659af65afdf6f completed March 7, 2026, 5:33 a.m.
NED1 Entity disambiguation (via context triple) batch_69b31983cab08190afcd168864c9ccf2 completed March 12, 2026, 7:52 p.m.
Created at: March 4, 2026, 7:37 p.m.