Triple

T1252184
Position Surface form Disambiguated ID Type / Status
Subject Red Dust E26901 entity
Predicate producer P490 FINISHED
Object Hunt Stromberg E168219 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: Hunt Stromberg | Statement: [Red Dust, producer, Hunt Stromberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hunt Stromberg
Context triple: [Red Dust, producer, Hunt Stromberg]
  • A. Hunt Stromberg chosen
    Hunt Stromberg was a prominent American film producer of Hollywood’s Golden Age, best known for his work at MGM on numerous classic films of the 1930s and 1940s.
  • B. George Strauss
    George Strauss was a British Labour politician who served for many years as a Member of Parliament and held senior government roles in the mid-20th century.
  • C. George Boemler
    George Boemler was a film editor known for his work on classic Hollywood productions, including the musical comedy "High Society."
  • D. Milton Van Dyke
    Milton Van Dyke was an influential American fluid dynamicist and author known for his classic works on aerodynamics and fluid mechanics, including the widely used reference "An Album of Fluid Motion."
  • E. Harold Hazen
    Harold Hazen was an American electrical engineer and MIT professor known for his pioneering work in control systems and his role in developing early analog computing devices.
  • 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_69a49487a9c48190ba9b05348fd1b53f completed March 1, 2026, 7:33 p.m.
NER Named-entity recognition batch_69a4bf875cf48190b6781d41097ee39b completed March 1, 2026, 10:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad293872cc8190894581cff289627e completed March 8, 2026, 7:46 a.m.
Created at: March 1, 2026, 7:47 p.m.