Triple

T2557733
Position Surface form Disambiguated ID Type / Status
Subject The Fatal Englishman E56766 entity
Predicate hasSubject P450 FINISHED
Object Christopher Wood E278252 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: Christopher Wood | Statement: [The Fatal Englishman, hasSubject, Christopher Wood]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Christopher Wood
Context triple: [The Fatal Englishman, hasSubject, Christopher Wood]
  • A. Christopher Wood chosen
    Christopher Wood was a talented but short-lived English painter and draughtsman associated with early 20th-century modernism, whose life and work are examined in the biography "The Fatal Englishman."
  • B. Jason Wood
    Jason Wood is a British film programmer, curator, and author known for his influential work in cinema programming and film culture.
  • C. Stuart Maynard
    Stuart Maynard is an English football manager best known for managing Notts County F.C. in the lower tiers of the English football league system.
  • D. Luke Rowan
    Luke Rowan is a fictional character from the works of 19th-century English novelist Anthony Trollope.
  • E. Michael Coulter
    Michael Coulter is a British cinematographer known for his work on popular films including the romantic comedy "Love Actually."
  • 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_69ab4a4bfec081908039988ec4c86e28 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd33153fc8190aa106e23ee645f63 completed March 7, 2026, 7:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69af655f72fc81908a85a69f95d0b827 completed March 10, 2026, 12:27 a.m.
Created at: March 6, 2026, 9:48 p.m.