Triple

T11299754
Position Surface form Disambiguated ID Type / Status
Subject Hair (1979 film) E267551 entity
Predicate notableSong P4 FINISHED
Object Hair E267551 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: Hair | Statement: [Hair (1979 film), notableSong, Hair]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hair
Context triple: [Hair (1979 film), notableSong, Hair]
  • A. Hair chosen
    Hair is a 1979 musical anti-war film directed by Miloš Forman, adapted from the 1960s stage musical and known for its portrayal of the hippie counterculture and Vietnam War–era America.
  • B. How To (Hair)
    "How To (Hair)" is a track from Esperanza Spalding’s experimental jazz album *12 Little Spells*, which blends innovative composition with conceptual, body-themed song titles.
  • C. Hair Body Face
    "Hair Body Face" is a pop song performed by Lady Gaga from the soundtrack of the 2018 film *A Star Is Born*.
  • D. The Hair Buyer
    The Hair Buyer is a character from the musical "Hamilton," known for purchasing Eliza Hamilton’s hair in the song "Burn."
  • E. Bangs
    Bangs is a surname of English origin borne by various notable individuals, including scientists, writers, and public figures.
  • 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_69d6aac993a08190a6f36445ebaf9a43 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e9a3616c8190a8fd23ca67463806 completed April 9, 2026, 6:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69e50a4af56881908cc395b6687d40a9 completed April 19, 2026, 5 p.m.
Created at: April 8, 2026, 9:32 p.m.