Triple

T20076969
Position Surface form Disambiguated ID Type / Status
Subject My Sister's Keeper E499892 entity
Predicate screenwriter P2831 FINISHED
Object Jeremy Leven 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: Jeremy Leven | Statement: [My Sister's Keeper, screenwriter, Jeremy Leven]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jeremy Leven
Context triple: [My Sister's Keeper, screenwriter, Jeremy Leven]
  • A. Jeremy Leven chosen
    Jeremy Leven is an American screenwriter, director, and novelist known for adapting romantic and character-driven stories for film, including the hit movie "The Notebook."
  • B. Jonathan Levien
    Jonathan Levien is the namesake of Levien Gymnasium, likely a significant benefactor or figure associated with the institution that houses the arena.
  • C. Bruce Weitz
    Bruce Weitz is an American actor best known for his Emmy-winning role as the eccentric detective Mick Belker on the television series "Hill Street Blues."
  • D. Lee Zahler
    Lee Zahler was an American film composer and musical director known for scoring numerous serials and B-movies during the 1930s and 1940s.
  • E. Michael Seitzman
    Michael Seitzman is an American screenwriter and producer known for his work on films such as "North Country" and for creating and producing several television series.
  • 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_69da627770948190997f486f9a2e370f completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e6643d5238819098b7d4e4e2c8dd67 completed April 20, 2026, 5:37 p.m.
Created at: April 11, 2026, 3:40 p.m.