Triple

T2241043
Position Surface form Disambiguated ID Type / Status
Subject Sir James Dyson E49395 entity
Predicate fullName P16 FINISHED
Object 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: James Dyson | Statement: [Sir James Dyson, fullName, James Dyson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: James Dyson
Context triple: [Sir James Dyson, fullName, 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. 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.
  • C. Christopher Cockerell
    Christopher Cockerell was a British engineer and inventor best known for creating the hovercraft.
  • 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_69a88aa979788190ad6500f1d8eee2fc completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc0be7fb4819081a5f9c46b616bdb completed March 7, 2026, 6:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae71bfb47881908c93333a9e808420 completed March 9, 2026, 7:07 a.m.
Created at: March 4, 2026, 7:47 p.m.