Triple

T19478831
Position Surface form Disambiguated ID Type / Status
Subject Barnwell E487324 entity
Predicate hasNotableBearer P458 FINISHED
Object John Barnwell 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: John Barnwell | Statement: [Barnwell, hasNotableBearer, John Barnwell]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Barnwell
Context triple: [Barnwell, hasNotableBearer, John Barnwell]
  • A. John Barnwell chosen
    John Barnwell was a prominent early South Carolina colonial leader and military figure whose legacy includes having Barnwell County named in his honor.
  • B. Frank Barnwell
    Frank Barnwell was a British aeronautical engineer and aircraft designer best known for creating several important World War I and interwar aircraft for the Bristol Aeroplane Company.
  • C. George Rooker
    George Rooker is known primarily as the husband of German-born voice actress and Bond film dubbing artist Nikki van der Zyl.
  • D. Charles Faulkner
    Charles Faulkner was a 19th-century British designer and partner in the influential Arts and Crafts firm Morris, Marshall, Faulkner & Co.
  • E. Talbot Jennings
    Talbot Jennings was an American screenwriter known for his work on notable mid-20th-century films, including several acclaimed literary and historical adaptations.
  • 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_69d8e8d924388190b847cb15bb3d0aff completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e63437b9748190a8fc6bf6b3d90918 completed April 20, 2026, 2:12 p.m.
Created at: April 10, 2026, 1:39 p.m.