Triple

T2317346
Position Surface form Disambiguated ID Type / Status
Subject Corsier-sur-Vevey E51095 entity
Predicate hasNotableResident P1092 FINISHED
Object Oona O’Neill E27480 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: Oona O’Neill | Statement: [Corsier-sur-Vevey, hasNotableResident, Oona O’Neill]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oona O’Neill
Context triple: [Corsier-sur-Vevey, hasNotableResident, Oona O’Neill]
  • A. Oona O’Neill chosen
    Oona O’Neill was an American-born actress and socialite, the daughter of playwright Eugene O’Neill, who became widely known as the longtime wife and muse of filmmaker Charlie Chaplin.
  • B. Virginia O'Brien
    Virginia O'Brien was an American film actress and singer best known for her deadpan comedic style and musical performances in MGM musicals of the 1940s.
  • C. Stella Damon
    Stella Damon is one of the daughters of American actor and filmmaker Matt Damon.
  • D. Helen O’Connell
    Helen O’Connell was a popular American big band singer and entertainer best known for her work with Jimmy Dorsey’s orchestra in the 1940s.
  • E. Karen O’Brien
    Karen O’Brien is a British academic and university leader who serves as Vice-Chancellor of Durham University, overseeing its strategic direction and academic mission.
  • 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_69a88b074b908190ae983dbca7757d88 completed March 4, 2026, 7:41 p.m.
NER Named-entity recognition batch_69abc62df2048190ac7a5ebc0a4139b2 completed March 7, 2026, 6:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae8964902081909070dd03ccb7cf1f completed March 9, 2026, 8:48 a.m.
Created at: March 4, 2026, 7:49 p.m.