Triple

T22447737
Position Surface form Disambiguated ID Type / Status
Subject Shadbolt E554906 entity
Predicate notableBearer P458 FINISHED
Object Nigel Shadbolt NE NERFINISHED

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: Nigel Shadbolt | Statement: [Shadbolt, notableBearer, Nigel Shadbolt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nigel Shadbolt
Context triple: [Shadbolt, notableBearer, Nigel Shadbolt]
  • A. Nigel Shadbolt chosen
    Nigel Shadbolt is a British computer scientist and artificial intelligence researcher known for his leading role in promoting open data and digital governance.
  • B. Nigel Sears
    Nigel Sears is a British tennis coach best known for working with several top WTA players, including former world No. 1 Ana Ivanovic.
  • C. Graham Dumpleton
    Graham Dumpleton is a software engineer and open-source developer best known for creating and maintaining mod_wsgi, a popular Apache module for hosting Python web applications.
  • D. Nigel Olifaunt
    Nigel Olifaunt is the fictional Scottish nobleman who serves as the protagonist of Sir Walter Scott’s novel "The Fortunes of Nigel."
  • E. Nigel Townsend
    Nigel Townsend is a quirky and brilliant British forensic criminalist on the television drama "Crossing Jordan," known for his dark humor and unconventional investigative methods.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e5113208190ab58c6b595f9d1d0 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15b48be0481909f4601b732424e5b completed April 29, 2026, 1:13 a.m.
Created at: April 16, 2026, 8:47 p.m.