Triple

T10525158
Position Surface form Disambiguated ID Type / Status
Subject Hannah Weinstein E248281 entity
Predicate employer P7 FINISHED
Object Sapphire Films E869352 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: Sapphire Films | Statement: [Hannah Weinstein, employer, Sapphire Films]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sapphire Films
Context triple: [Hannah Weinstein, employer, Sapphire Films]
  • A. Sapphire Films chosen
    Sapphire Films was a British television and film production company best known for creating popular 1950s adventure series such as "The Adventures of Robin Hood."
  • B. Diamond Films
    Diamond Films is a film production company co-founded by Welsh actor and producer Stanley Baker, known for its involvement in British cinema projects.
  • C. Scion Films
    Scion Films is a British film production company known for backing acclaimed dramas such as "The Constant Gardener."
  • D. Fortis Films
    Fortis Films is a film and television production company founded by actress Sandra Bullock, known for producing projects such as the Miss Congeniality films.
  • E. Efficeon
    Efficeon is a family of low-power, x86-compatible microprocessors developed by Transmeta as the successor to its Crusoe line, aimed primarily at mobile and embedded computing devices.
  • 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_69d381c5c7448190bec34bee7ec72bac completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d509f4020c8190b78c49da086df757 completed April 7, 2026, 1:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69d933f2d9e48190a4c5d5d5bdc0d7d8 completed April 10, 2026, 5:31 p.m.
Created at: April 6, 2026, 12:29 p.m.