Triple

T8276209
Position Surface form Disambiguated ID Type / Status
Subject Muller v. Oregon brief E193552 entity
Predicate genderPerspective P60410 FINISHED
Object framed women as needing special protection 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: framed women as needing special protection | Statement: [Muller v. Oregon brief, genderPerspective, framed women as needing special protection]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: genderPerspective
Context triple: [Muller v. Oregon brief, genderPerspective, framed women as needing special protection]
  • A. genderImplication
    Indicates that one entity’s gender suggests, constrains, or determines the possible or likely gender of another entity.
  • B. genderCategories
    Indicates the classification of an entity into one or more gender-related categories or identities.
  • C. genderNorms chosen
    Indicates socially constructed expectations or rules about how individuals should behave, appear, or identify based on their perceived gender.
  • D. genderSignificance
    Indicates the relevance or impact that an entity’s gender has within a particular context, relationship, or interpretation.
  • E. genderConfiguration
    Indicates how the genders of the involved entities are arranged or combined within a particular relationship or context.
  • 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_69ca82e14ae481908ffdb822cd2192bc completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb798d69508190b581ad8a38730175 completed March 31, 2026, 7:36 a.m.
PD Predicate disambiguation batch_69cb70a4525481909399d313a6247ace completed March 31, 2026, 6:58 a.m.
Created at: March 30, 2026, 5:51 p.m.