Triple

T16763772
Position Surface form Disambiguated ID Type / Status
Subject Daytona Normal and Industrial Institute E407410 entity
Predicate laterGenderPolicy P277 FINISHED
Object coeducational 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: coeducational | Statement: [Daytona Normal and Industrial Institute, laterGenderPolicy, coeducational]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: laterGenderPolicy
Context triple: [Daytona Normal and Industrial Institute, laterGenderPolicy, coeducational]
  • A. hasGenderPolicy chosen
    Indicates that an entity has adopted, implemented, or is governed by a specific policy related to gender issues or gender equality.
  • B. governsGender
    Indicates that one entity determines or constrains the gender classification or gender-related properties of another entity.
  • C. demographicPolicy
    Indicates a relationship where an authority or organization establishes or applies rules and measures intended to influence the size, structure, or composition of a population.
  • D. hasGenderNeutrality
    Indicates that something (such as a term, form, or expression) is neutral with respect to gender and does not specify or imply any particular gender.
  • E. genderNeutralSince
    Indicates that an entity has been considered gender-neutral starting from a specific point in time.
  • F. None of above.

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_69d8839174188190909f190097207065 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3abef492c8190880d3b39c3641eed completed April 18, 2026, 4:06 p.m.
PD Predicate disambiguation batch_69e319cbd79c8190a03587a61c18bec0 completed April 18, 2026, 5:42 a.m.
Created at: April 10, 2026, 5:21 a.m.