Triple

T22573763
Position Surface form Disambiguated ID Type / Status
Subject LOL (2006 film) E544339 entity
Predicate musicBy P1952 FINISHED
Object Kevin Bewersdorf 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: Kevin Bewersdorf | Statement: [LOL (2006 film), musicBy, Kevin Bewersdorf]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kevin Bewersdorf
Context triple: [LOL (2006 film), musicBy, Kevin Bewersdorf]
  • A. Kevin Bewersdorf chosen
    Kevin Bewersdorf is an American artist, musician, and actor associated with the early mumblecore film movement and known for his work in experimental cinema and internet art.
  • B. Eric L. Zinterhofer
    Eric L. Zinterhofer is an American private equity investor and media executive known for his leadership roles in the telecommunications and cable industry.
  • C. Michael J. Pierson
    Michael J. Pierson is an individual notable enough to be specifically cited as a namesake or distinguished bearer of the surname Pierson.
  • D. John Boettiger
    John Boettiger was an American journalist and newspaper publisher best known as the second husband of Anna Roosevelt, daughter of President Franklin D. Roosevelt.
  • E. Kevin Biegel
    Kevin Biegel is an American television writer and producer best known for co-creating the sitcom Cougar Town and working on shows like Scrubs and Enlisted.
  • 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_69e11e30d05481909df915354c89f0d6 completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f15fea683c81908fbf9f171eed3341 completed April 29, 2026, 1:33 a.m.
Created at: April 16, 2026, 8:53 p.m.