Triple

T19796246
Position Surface form Disambiguated ID Type / Status
Subject Adrian (costume designer) E475547 entity
Predicate employer P7 FINISHED
Object MGM Studios NE NERFINISHED

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: MGM Studios | Statement: [Adrian (costume designer), employer, MGM Studios]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: MGM Studios
Context triple: [Adrian (costume designer), employer, MGM Studios]
  • A. MGM chosen
    MGM (Metro-Goldwyn-Mayer) is a historic American film studio renowned for its iconic roaring lion logo and for producing many of the most famous movies of Hollywood’s Golden Age.
  • B. MGM
    MGM is the three-letter National Rail station code assigned to Metheringham railway station in Lincolnshire, England.
  • C. MGM
    MGM is a major American entertainment and hospitality brand best known for its iconic casinos, resorts, and film studio legacy.
  • D. MGM
    MGM is the IATA airport code for Montgomery Regional Airport, the primary commercial airport serving Montgomery, Alabama.
  • E. MGM
    MGM is the three-letter FAA location identifier assigned to Harbor Springs Municipal Airport in Michigan.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e51b014081908b263e167370529a completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e653c723548190ac9bfaecaf8afb13 completed April 20, 2026, 4:26 p.m.
Created at: April 10, 2026, 1:49 p.m.