Triple

T10812008
Position Surface form Disambiguated ID Type / Status
Subject Two Tickets to London E255124 entity
Predicate castMember P1668 FINISHED
Object Charles Irwin E871091 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: Charles Irwin | Statement: [Two Tickets to London, castMember, Charles Irwin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Charles Irwin
Context triple: [Two Tickets to London, castMember, Charles Irwin]
  • A. Charles Irwin chosen
    Charles Irwin was the husband of American actress Helen Mack, known primarily in relation to her career in early 20th-century film and radio.
  • B. Cecil Irwin
    Cecil Irwin was an English professional footballer and long-serving right-back for Sunderland AFC during the mid-20th century.
  • C. William Irwin
    William Irwin was a 19th-century American politician who served as Governor of California from 1875 to 1880.
  • D. Henry Irwin
    Henry Irwin was a British architect of the late 19th and early 20th centuries known for his prominent public buildings in colonial India, particularly in the Indo-Saracenic style.
  • E. John L. Lumley
    John L. Lumley was a prominent American fluid dynamicist known for his pioneering contributions to the understanding and modeling of turbulence.
  • 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_69d6aa61c15c8190a1839550c56e75e1 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d733eadda48190b2b1183ee60102cb completed April 9, 2026, 5:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69e5e877c6188190817fb30f2c9a07bf completed April 20, 2026, 8:48 a.m.
Created at: April 8, 2026, 9:18 p.m.