Triple

T276917
Position Surface form Disambiguated ID Type / Status
Subject Trump University E5268 entity
Predicate notableDuring P8452 FINISHED
Object 2016 United States presidential election 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: 2016 United States presidential election | Statement: [Trump University, notableDuring, 2016 United States presidential election]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: notableDuring
Context triple: [Trump University, notableDuring, 2016 United States presidential election]
  • A. notableFor
    Indicates that an entity is especially recognized or distinguished for a particular quality, achievement, characteristic, or role.
  • B. notableSingle
    Indicates that the subject is particularly recognized or distinguished for one specific, individual instance (such as a single work, event, or achievement).
  • C. notablePrimary
    Indicates that one entity is the main or most prominent example, instance, or representative of another entity.
  • D. notableElement
    Indicates that an entity has a component, feature, or part that is especially significant, prominent, or noteworthy in relation to it.
  • E. notableCategory
    Indicates that an entity is recognized as notable or significant within a particular category or classification.
  • 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_69a257e6c8788190987dfe705ca2912a completed Feb. 28, 2026, 2:50 a.m.
NER Named-entity recognition batch_69a25ded68c88190b1fc595ce329aeb9 completed Feb. 28, 2026, 3:15 a.m.
PD Predicate disambiguation batch_69a25b7480e881909399beccfc7ffb81 completed Feb. 28, 2026, 3:05 a.m.
PDg Predicate description generation batch_69a25c2d94388190aeda17ddd42b4ac9 completed Feb. 28, 2026, 3:08 a.m.
Created at: Feb. 28, 2026, 2:59 a.m.