Triple

T3849036
Position Surface form Disambiguated ID Type / Status
Subject Laurie Pritchett E85244 entity
Predicate lawEnforcementPhilosophy P21773 FINISHED
Object maintaining segregation while avoiding overt brutality 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: maintaining segregation while avoiding overt brutality | Statement: [Laurie Pritchett, lawEnforcementPhilosophy, maintaining segregation while avoiding overt brutality]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: lawEnforcementPhilosophy
Context triple: [Laurie Pritchett, lawEnforcementPhilosophy, maintaining segregation while avoiding overt brutality]
  • A. policingModel chosen
    Indicates the approach, strategy, or framework used to organize and conduct policing activities or law enforcement operations.
  • B. typeOfLawEnforcement
    Indicates that one entity is a specific kind or category of law enforcement associated with another entity.
  • C. legalPhilosophy
    Indicates the philosophical principles, theories, or viewpoints that underpin or guide a legal system, doctrine, or interpretation.
  • D. lawEnforcementLevel
    Indicates the degree or intensity of law enforcement presence, activity, or strictness applied in a given context.
  • E. impactOnLawEnforcement
    Indicates the effect or consequences that something has on law enforcement activities, operations, or effectiveness.
  • 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_69aed936de1c81908f91bed80f70abb2 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeebcde86081908cf3840ae002acfa completed March 9, 2026, 3:48 p.m.
PD Predicate disambiguation batch_69aee750377c8190af70c79768c0edd8 completed March 9, 2026, 3:29 p.m.
Created at: March 9, 2026, 3:19 p.m.