Triple

T14391499
Position Surface form Disambiguated ID Type / Status
Subject All This, and Heaven Too (1940 film) E356853 entity
Predicate castMember P1668 FINISHED
Object Jeffrey Lynn E391237 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: Jeffrey Lynn | Statement: [All This, and Heaven Too (1940 film), castMember, Jeffrey Lynn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jeffrey Lynn
Context triple: [All This, and Heaven Too (1940 film), castMember, Jeffrey Lynn]
  • A. Jeffrey Lynn chosen
    Jeffrey Lynn was an American film and stage actor best known for his roles in 1930s and 1940s Hollywood dramas and romances.
  • B. Jeffrey Byron
    Jeffrey Byron is an American actor known for his work in film and television since the 1960s, including roles in genre and action productions.
  • C. Jeffrey Heath
    Jeffrey Heath is a linguist renowned for his extensive fieldwork and documentation of Dogon and other African languages.
  • D. Jeffrey Hayden
    Jeffrey Hayden was an American television and film director known for his extensive work in mid-20th-century TV dramas and variety shows.
  • E. Jeffrey Lynn Green
    Jeffrey Lynn Green is an American former professional basketball player and current NBA forward known for his versatility and long career with multiple teams.
  • 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_69d827927c988190ad98bb0360981783 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de902b9acc8190817ffa848a76a880 completed April 14, 2026, 7:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff56afa5ec8190a058574dff7431dc completed May 9, 2026, 3:45 p.m.
Created at: April 10, 2026, 1:16 a.m.