Triple

T22946847
Position Surface form Disambiguated ID Type / Status
Subject Wind E569896 entity
Predicate screenwriter P2831 FINISHED
Object Roger Vaughan
Roger Vaughan is a screenwriter known for his work on the film "Wind," a drama centered on competitive sailing.
 E1564205 NE FINISHED

How this triple was built (4 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: Roger Vaughan | Statement: [Wind, screenwriter, Roger Vaughan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Roger Vaughan
Context triple: [Wind, screenwriter, Roger Vaughan]
  • A. Roger Vaughan
    Roger Vaughan was a 19th-century English Benedictine monk who became the second Roman Catholic Archbishop of Sydney, Australia.
  • B. John Lyons
    John Lyons is a film producer best known for his work on major Hollywood comedies, including the Austin Powers series.
  • C. John Lyons
    John Lyons was a prominent British linguist and semanticist known for his influential work on theoretical linguistics and the philosophy of language.
  • D. John Lyons
    John Lyons is a film producer known for his work on the acclaimed documentary "All the Beauty and the Bloodshed."
  • E. Frank Wynne
    Frank Wynne is an Irish literary translator and writer renowned for bringing numerous French and Spanish-language authors to English-speaking audiences.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Roger Vaughan
Triple: [Wind, screenwriter, Roger Vaughan]
Generated description
Roger Vaughan is a screenwriter known for his work on the film "Wind," a drama centered on competitive sailing.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Roger Vaughan
Target entity description: Roger Vaughan is a screenwriter known for his work on the film "Wind," a drama centered on competitive sailing.
  • A. Roger Vaughan
    Roger Vaughan was a 19th-century English Benedictine monk who became the second Roman Catholic Archbishop of Sydney, Australia.
  • B. John Lyons
    John Lyons is a film producer best known for his work on major Hollywood comedies, including the Austin Powers series.
  • C. John Lyons
    John Lyons was a prominent British linguist and semanticist known for his influential work on theoretical linguistics and the philosophy of language.
  • D. John Lyons
    John Lyons is a film producer known for his work on the acclaimed documentary "All the Beauty and the Bloodshed."
  • E. Frank Wynne
    Frank Wynne is an Irish literary translator and writer renowned for bringing numerous French and Spanish-language authors to English-speaking audiences.
  • F. None of above. chosen

Provenance (5 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_69e2459199d08190a8184ee2aa935842 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1819e559c81909e63acfc23f9476b completed April 29, 2026, 3:57 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0bca1437d081909227488e480a69a5 completed May 19, 2026, 2:25 a.m.
NEDg Description generation batch_6a0bcca3edf081908254a31bb0f5576e completed May 19, 2026, 2:36 a.m.
NED2 Entity disambiguation (via description) batch_6a0bcd53eecc81909ac4ec82da07bc6b completed May 19, 2026, 2:39 a.m.
Created at: April 17, 2026, 3:46 p.m.