Triple

T22956999
Position Surface form Disambiguated ID Type / Status
Subject Dead Men Don’t Wear Plaid E570786 entity
Predicate writer P1360 FINISHED
Object George Gipe NE NERFINISHED

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: George Gipe | Statement: [Dead Men Don’t Wear Plaid, writer, George Gipe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: George Gipe
Context triple: [Dead Men Don’t Wear Plaid, writer, George Gipe]
  • A. George Gipe chosen
    George Gipe was an American screenwriter and author known for his work on comedic and genre films in the late 20th century.
  • B. George Hudson
    George Hudson was a 19th-century English railway financier and politician, famously known as the "Railway King" for his dominant role in the early development and expansion of Britain's railway network.
  • C. Lewis Pilcher
    Lewis Pilcher was an American architect known for designing prominent military and civic structures in New York, including major armories.
  • D. George Riddle
    George Riddle was an American character actor known for his supporting roles in film and television, including a part in the horror film "The Innkeepers."
  • E. Ian Furner
    Ian Furner is an academic known for supervising the doctoral research of prominent plant biologist Dame Ottoline Leyser.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e245b212a88190b5259caf51606084 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f181f15ebc8190bf477539f7c23985 completed April 29, 2026, 3:58 a.m.
Created at: April 17, 2026, 3:47 p.m.