Triple

T13764764
Position Surface form Disambiguated ID Type / Status
Subject Kathryn Ann Bailey E330709 entity
Predicate hasPositionHeldThroughNamesake P110871 FINISHED
Object United States senator from Texas 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: United States senator from Texas | Statement: [Kathryn Ann Bailey, hasPositionHeldThroughNamesake, United States senator from Texas]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasPositionHeldThroughNamesake
Context triple: [Kathryn Ann Bailey, hasPositionHeldThroughNamesake, United States senator from Texas]
  • A. officeHoldersAlsoHeldPosition
    Indicates that an individual who holds one office or position has also held another specified office or position (at some time, possibly earlier or later).
  • B. hasHistoricalOfficeHolder
    Indicates that an office, position, or role has been held by a specific person at some point in the past.
  • C. hasLeaderHeldOffice
    Indicates that the specified leader has occupied or served in an official office or position.
  • D. precededByOfficeHolder
    Indicates that one office holder directly held a position before another office holder in a sequence of occupants of the same office.
  • E. heldOfficeContinuously
    Indicates that an individual occupied a particular office or position without interruption over a specified period of time.
  • 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_69d81c583b0081909e408a17db517a21 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de022690ac8190bd5410ecc659a2a7 completed April 14, 2026, 9 a.m.
PD Predicate disambiguation batch_69dbbe97846c819093b00ea117b64e0d completed April 12, 2026, 3:47 p.m.
PDg Predicate description generation batch_69dbc59db0148190bcaf9646403ca64f completed April 12, 2026, 4:17 p.m.
Created at: April 9, 2026, 10:10 p.m.