Triple

T4291866
Position Surface form Disambiguated ID Type / Status
Subject Corpus E99611 entity
Predicate hasNotableAlumni P51 FINISHED
Object Sir James Dyson E49395 NE 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: Sir James Dyson | Statement: [Corpus, hasNotableAlumni, Sir James Dyson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sir James Dyson
Context triple: [Corpus, hasNotableAlumni, Sir James Dyson]
  • A. Sir James Dyson chosen
    Sir James Dyson is a British inventor and industrial designer best known for creating the Dyson bagless vacuum cleaner and founding the Dyson technology company.
  • B. Christopher Cockerell
    Christopher Cockerell was a British engineer and inventor best known for creating the hovercraft.
  • C. Dean Kamen
    Dean Kamen is an American inventor and entrepreneur best known for creating the Segway and numerous medical technologies, and for founding the FIRST robotics competition to inspire young people in science and engineering.
  • D. James Dyson Foundation
    The James Dyson Foundation is a charitable organization that supports design and engineering education and innovation, particularly among young people.
  • E. Thomas Beeby
    Thomas Beeby is an American architect associated with the New Classical movement, known for designing prominent cultural and institutional buildings.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69b3455175088190aa79c6e03b86647e completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3508035a08190b752c8edce0aff86 completed March 12, 2026, 11:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5c73501f4819088bf87f3c4d0f23b completed March 14, 2026, 8:38 p.m.
Created at: March 12, 2026, 11:08 p.m.