Triple

T9069396
Position Surface form Disambiguated ID Type / Status
Subject One Hundred Easy Ways E217325 entity
Predicate lyricist P1360 FINISHED
Object Adolph Green E71773 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: Adolph Green | Statement: [One Hundred Easy Ways, lyricist, Adolph Green]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Adolph Green
Context triple: [One Hundred Easy Ways, lyricist, Adolph Green]
  • A. Adolph Green chosen
    Adolph Green was an American playwright, lyricist, and screenwriter best known for his long collaboration with Betty Comden on classic Broadway musicals and Hollywood films.
  • B. Otto Hunte
    Otto Hunte was a prominent German film art director and production designer best known for his influential work on classic Weimar-era films, including Fritz Lang’s Metropolis.
  • C. Lewis Allen
    Lewis Allen was a British-born film and television director best known for his atmospheric work in mid-20th-century Hollywood cinema.
  • D. Lewis Allen
    Lewis Allen was a local figure of significance after whom the city of Allen Park, Michigan, was named.
  • E. Charles Bickford
    Charles Bickford was an American character actor known for his rugged screen presence and acclaimed supporting roles in numerous classic Hollywood films.
  • 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_69ca83d5a7f48190b16c1e59bd43ede0 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cc955ba250819085fa49e0059d06c1 completed April 1, 2026, 3:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69d139a887b0819088485006f1653d1c completed April 4, 2026, 4:17 p.m.
Created at: March 30, 2026, 7:11 p.m.