Triple

T14413870
Position Surface form Disambiguated ID Type / Status
Subject The Hospital E357398 entity
Predicate starring P1507 FINISHED
Object Barnard Hughes E686836 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: Barnard Hughes | Statement: [The Hospital, starring, Barnard Hughes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Barnard Hughes
Context triple: [The Hospital, starring, Barnard Hughes]
  • A. Barnard Hughes chosen
    Barnard Hughes was an American character actor known for his work in film, television, and theater, often playing kindly or eccentric older men.
  • B. Francis Hughes
    Francis Hughes was a Provisional Irish Republican Army volunteer from Northern Ireland who became widely known for dying on hunger strike in the Maze Prison in 1981.
  • C. Rutherford Alcock
    Rutherford Alcock was a 19th-century British diplomat best known as the first British consul in Japan and for helping open the country to Western influence.
  • D. George Buckley
    George Buckley is a British businessman best known for serving as the chairman and CEO of 3M.
  • E. Sidney Lanfield
    Sidney Lanfield was an American film and television director best known for his work on Hollywood comedies and genre films from the 1930s through the 1950s.
  • 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_69d82793421c8190861eb0e673b085de completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de90cb3c708190822f5506ebf7ee9d completed April 14, 2026, 7:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe9db810c481908dde925ff90f3fa0 completed May 9, 2026, 2:36 a.m.
Created at: April 10, 2026, 1:17 a.m.