Triple

T66645
Position Surface form Disambiguated ID Type / Status
Subject Robert F. Kennedy's 1968 presidential campaign E1329 entity
Predicate positionOnCrime P4173 FINISHED
Object emphasized addressing root causes of crime 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: emphasized addressing root causes of crime | Statement: [Robert F. Kennedy's 1968 presidential campaign, positionOnCrime, emphasized addressing root causes of crime]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: positionOnCrime
Context triple: [Robert F. Kennedy's 1968 presidential campaign, positionOnCrime, emphasized addressing root causes of crime]
  • A. positionOn
    Indicates that one entity is located on top of or at a specific place along the surface or extent of another entity.
  • B. positionHeld
    Indicates that an entity occupies or has occupied a specific role, job, office, or position within an organization or context.
  • C. policePrecinct
    Indicates that a specified location, building, or area functions as or is designated as a police precinct.
  • D. subjectPosition
    Indicates the spatial or logical position of a subject relative to a reference frame, context, or other entities.
  • E. locatedAlong
    Indicates that one entity is situated adjacent to, or running beside, the length or course of another linear feature (such as a road, river, or railway).
  • 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_69a24ba4f760819081f6638a3c70538a completed Feb. 28, 2026, 1:57 a.m.
NER Named-entity recognition batch_69a2509b5a088190bb9d2b650aeb8bca completed Feb. 28, 2026, 2:19 a.m.
PD Predicate disambiguation batch_69a24ea749788190bc17865171ff909a completed Feb. 28, 2026, 2:10 a.m.
PDg Predicate description generation batch_69a2509a1c088190b4afa3045455709a completed Feb. 28, 2026, 2:19 a.m.
Created at: Feb. 28, 2026, 2:02 a.m.