Triple

T12449017
Position Surface form Disambiguated ID Type / Status
Subject Private Parts E297480 entity
Predicate screenwriter P2831 FINISHED
Object Michael Kalesniko E543371 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: Michael Kalesniko | Statement: [Private Parts, screenwriter, Michael Kalesniko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Kalesniko
Context triple: [Private Parts, screenwriter, Michael Kalesniko]
  • A. Michael Kalesniko chosen
    Michael Kalesniko is a Canadian screenwriter and film director known for his darkly comedic and character-driven work in independent cinema.
  • B. Calvin Wimmer
    Calvin Wimmer is a film editor best known for his work on the science fiction horror movie "The Cloverfield Paradox."
  • C. Aaron Korsh
    Aaron Korsh is an American television writer and producer best known for creating the legal drama series "Suits."
  • D. Mike Kuchar
    Mike Kuchar is an American underground filmmaker and artist known for his campy, low-budget, and highly influential experimental films that helped define New York’s underground cinema scene in the 1960s.
  • E. Max Zaritsky
    Max Zaritsky was an American labor leader and union organizer who played a key role in the early development of industrial unionism in the United States.
  • 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_69d6ada166c48190b902972cd2408fa3 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94d9e592c81908cf7f3ca170d942c completed April 10, 2026, 7:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f65ea710d481908371209cb92502a6 completed May 2, 2026, 8:29 p.m.
Created at: April 8, 2026, 9:56 p.m.