Triple

T15059871
Position Surface form Disambiguated ID Type / Status
Subject Burke E379595 entity
Predicate hasNotableBearer P458 FINISHED
Object Thomas Burke E947107 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: Thomas Burke | Statement: [Burke, hasNotableBearer, Thomas Burke]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Thomas Burke
Context triple: [Burke, hasNotableBearer, Thomas Burke]
  • A. Thomas Burke
    Thomas Burke was an American sprinter who became the first Olympic champion in both the 100-meter and 400-meter races at the modern Games.
  • B. Thomas Burke
    Thomas Burke was an American politician who served as the third Governor of North Carolina during the early years of the United States.
  • C. Thomas Burke
    Thomas Burke was a British author best known for his early 20th-century stories set in London’s East End, including the tale that inspired the film "Broken Blossoms."
  • D. Thomas Burke chosen
    Thomas Burke is a common personal name shared by numerous individuals across various fields, including politics, sports, literature, and the arts.
  • E. George Barr McCutcheon
    George Barr McCutcheon was an American novelist best known for his popular early 20th-century works such as the novel "Brewster's Millions," which inspired numerous film adaptations.
  • 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_69d85cd64d108190853797a95c11cc45 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69dedee50afc8190bf7b0f4bbe8c60a3 completed April 15, 2026, 12:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69fea5c2fcec8190800d1bda82c7352e completed May 9, 2026, 3:10 a.m.
Created at: April 10, 2026, 3:01 a.m.