Triple

T40071
Position Surface form Disambiguated ID Type / Status
Subject Nobel Prize in Literature E791 entity
Predicate hasGenderDistributionIssues P2733 FINISHED
Object yes 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: yes | Statement: [Nobel Prize in Literature, hasGenderDistributionIssues, yes]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasGenderDistributionIssues
Context triple: [Nobel Prize in Literature, hasGenderDistributionIssues, yes]
  • A. hasGenderPolicy
    Indicates that an entity has adopted, implemented, or is governed by a specific policy related to gender issues or gender equality.
  • B. hasNumberOfGenders
    Indicates the relationship that specifies how many distinct genders are associated with or recognized for a given entity.
  • C. hasGenderedTitle
    Indicates that an entity is associated with a title or form of address that is explicitly marked for a particular gender.
  • D. hasSignificantPopulationGroup
    Indicates that an entity contains or is associated with a notable or substantial subgroup of a population, distinguished by shared characteristics or attributes.
  • E. sexOrGender
    Indicates that one entity has a specified biological sex or socially constructed gender identity.
  • 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_69a247a8f6c08190bac804906d62ed5a completed Feb. 28, 2026, 1:40 a.m.
NER Named-entity recognition batch_69a24b80f4a8819090d2bffe29824b90 completed Feb. 28, 2026, 1:57 a.m.
PD Predicate disambiguation batch_69a24ab74c548190a54872e15c8394c3 completed Feb. 28, 2026, 1:53 a.m.
PDg Predicate description generation batch_69a24b7fd2c08190a0057fe7aec6a1ee completed Feb. 28, 2026, 1:57 a.m.
Created at: Feb. 28, 2026, 1:46 a.m.