Triple

T12934166
Position Surface form Disambiguated ID Type / Status
Subject Daylight E309460 entity
Predicate screenwriter P2831 FINISHED
Object Leslie Bohem E385709 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: Leslie Bohem | Statement: [Daylight, screenwriter, Leslie Bohem]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Leslie Bohem
Context triple: [Daylight, screenwriter, Leslie Bohem]
  • A. Leslie Bohem chosen
    Leslie Bohem is an American screenwriter and producer known for his work on science fiction and thriller films and television projects.
  • B. Leslie Marr
    Leslie Marr was a British landscape painter and amateur racing driver known for competing in Formula One in the 1950s while pursuing a successful artistic career.
  • C. Connee Boswell
    Connee Boswell was an American jazz and popular music singer, best known as a member of the Boswell Sisters and for her influential solo recordings in the 1930s and 1940s.
  • D. Lila Leslie
    Lila Leslie was a silent film actress active in the early 20th century, known for her roles in American dramas and melodramas.
  • E. Betsy Rue
    Betsy Rue is an American actress best known for her roles in horror and thriller films, including her appearance in the slasher movie "My Bloody Valentine 3D."
  • 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_69d7bdfa933c8190b5a27aa4a08a19b7 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d97dc6517481908637781da240b51f completed April 10, 2026, 10:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6af6d16388190abc848ac67bf1fb9 completed May 3, 2026, 2:14 a.m.
Created at: April 9, 2026, 5:42 p.m.