Triple

T13236065
Position Surface form Disambiguated ID Type / Status
Subject Send Me No Flowers E315149 entity
Predicate producer P490 FINISHED
Object Harry Keller E658667 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: Harry Keller | Statement: [Send Me No Flowers, producer, Harry Keller]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Harry Keller
Context triple: [Send Me No Flowers, producer, Harry Keller]
  • A. Harry Keller chosen
    Harry Keller was an American film editor and director known for his work on numerous mid-20th-century Hollywood productions.
  • B. Mark O’Brien
    Mark O’Brien is an American local politician who has served as the mayor of Augusta, Maine.
  • C. Mark O'Brien
    Mark O'Brien is an American actor best known for his roles in television series such as City on a Hill and Halt and Catch Fire, as well as various film and independent projects.
  • D. Laura Bridgman
    Laura Bridgman was a pioneering 19th-century American deafblind woman who became the first such person to receive a significant formal education and gain widespread public attention.
  • E. Mary Ellen Sullivan
    Mary Ellen Sullivan was the wife of American actor and boxer Max Baer Sr., known primarily for her role in his personal life rather than for a public career of her own.
  • 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_69d806affc688190a25b6ccc588e9c72 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98d56da008190af55da3a9e7ffd4d completed April 10, 2026, 11:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6ff3079c08190977663e5d4762a80 completed May 3, 2026, 7:54 a.m.
Created at: April 9, 2026, 9:22 p.m.