Triple

T34598168
Position Surface form Disambiguated ID Type / Status
Subject Nicole Burnell E888374 entity
Predicate relationshipToLawyer P205477 FINISHED
Object key witness for Mitchell Stephens 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: key witness for Mitchell Stephens | Statement: [Nicole Burnell, relationshipToLawyer, key witness for Mitchell Stephens]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: relationshipToLawyer
Context triple: [Nicole Burnell, relationshipToLawyer, key witness for Mitchell Stephens]
  • A. legalCaseRole
    Indicates the specific role or capacity an entity holds within a legal case, such as plaintiff, defendant, judge, or attorney.
  • B. relationshipToDoctor
    Indicates the specific personal or professional connection an individual has with a doctor (e.g., self, spouse, parent, guardian, colleague).
  • C. courtRelationship
    Indicates a formal legal relationship between parties established or recognized through court proceedings or judicial decisions.
  • D. lawPartnerOf
    Indicates a professional relationship in which two individuals are partners in the same law firm or legal practice.
  • E. hasProfessionalRelationshipWith
    Indicates a formal, work-related connection or collaboration exists between the two entities in a professional context.
  • 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_69f349d3bfcc81909874c99e646fb3ea completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_6a037c92f03c8190ae2751270b195423 completed May 12, 2026, 7:16 p.m.
PD Predicate disambiguation batch_6a0379fd7aac8190873077e63873aa72 completed May 12, 2026, 7:05 p.m.
PDg Predicate description generation batch_6a037c80ba448190853011097a151b7e completed May 12, 2026, 7:16 p.m.
Created at: May 1, 2026, 2:03 a.m.