Triple

T10504420
Position Surface form Disambiguated ID Type / Status
Subject Byam Shaw School of Art E247748 entity
Predicate notableAlumni 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: [Byam Shaw School of Art, notableAlumni, Sir James Dyson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sir James Dyson
Context triple: [Byam Shaw School of Art, notableAlumni, 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. Tony Fadell
    Tony Fadell is an American engineer, designer, and entrepreneur best known as a key creator of the iPod and iPhone and a pioneer in modern smart home technology.
  • D. 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.
  • E. John Duttine
    John Duttine is an English actor known for his extensive work in British television drama, including prominent roles in series such as "To Serve Them All My Days" and "Heartbeat."
  • 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_69d381c4aa948190942e1d803143fb0e completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d5099f4dec8190a9851739c8bc9a69 completed April 7, 2026, 1:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69d97a05ffdc8190a69b39c807a06042 completed April 10, 2026, 10:30 p.m.
Created at: April 6, 2026, 12:26 p.m.