Triple

T10979510
Position Surface form Disambiguated ID Type / Status
Subject Phipps E259462 entity
Predicate hasNotableBearer P458 FINISHED
Object George Phipps E343034 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: George Phipps | Statement: [Phipps, hasNotableBearer, George Phipps]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: George Phipps
Context triple: [Phipps, hasNotableBearer, George Phipps]
  • A. George Phipps chosen
    George Phipps was an individual significant enough in local history that the town of Georgetown, California, was named in his honor.
  • B. Frederick Keppel
    Frederick Keppel was an 18th-century English clergyman who became Bishop of Exeter and served as Dean of Windsor and Registrar of the Order of the Garter.
  • C. James Frecheville
    James Frecheville is an Australian actor best known for his breakout lead role in the crime drama film "Animal Kingdom."
  • D. William Phipps
    William Phipps was an American actor best known for providing the speaking voice of Prince Charming in Disney’s animated classic "Cinderella" (1950).
  • E. George Staunton
    George Staunton is a central figure in Sir Walter Scott’s novel "The Heart of Midlothian," known for his complex, morally ambiguous role that intertwines crime, guilt, and redemption.
  • 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_69d6aa895f4c8190887a15460ef622f4 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d771f7b874819087bf5a858905279b completed April 9, 2026, 9:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69e2d7cb8fe08190b9de7b970968da48 completed April 18, 2026, 1 a.m.
Created at: April 8, 2026, 9:24 p.m.