Triple

T433460
Position Surface form Disambiguated ID Type / Status
Subject Academy Award for Best Director E9761 entity
Predicate hasNotableFirstFemaleWinnerYear P13907 FINISHED
Object 2010 ceremony (for 2009 films) LITERAL 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: 2010 ceremony (for 2009 films) | Statement: [Academy Award for Best Director, hasNotableFirstFemaleWinnerYear, 2010 ceremony (for 2009 films)]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasNotableFirstFemaleWinnerYear
Context triple: [Academy Award for Best Director, hasNotableFirstFemaleWinnerYear, 2010 ceremony (for 2009 films)]
  • A. isFirstFemaleHolderOfOffice
    Indicates that a person is the first woman ever to hold a particular office or position.
  • B. firstAwarded
    Indicates the time or occasion when an award, honor, or recognition was given for the very first time.
  • C. notableWinner
    Indicates that the subject is a particularly distinguished or prominent winner of the referenced competition, award, or contest.
  • D. notableMultipleWinners
    Indicates that the subject has achieved multiple wins or repeated successes in a notable event, competition, or award.
  • E. hasFemaleEquivalent
    Indicates that one entity serves as the female counterpart or equivalent of another entity.
  • F. None of above. chosen

Provenance (4 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_69a2e801e1d48190b505d1dd336b52ac completed Feb. 28, 2026, 1:05 p.m.
NER Named-entity recognition batch_69a2ef084840819080653004b674cba8 completed Feb. 28, 2026, 1:35 p.m.
PD Predicate disambiguation batch_69a2edda55e88190b7c17ba94d7df1ce completed Feb. 28, 2026, 1:30 p.m.
PDg Predicate description generation batch_69a2eeb93584819082f23eff13e17c4f completed Feb. 28, 2026, 1:33 p.m.
Created at: Feb. 28, 2026, 1:11 p.m.