Triple

T3859899
Position Surface form Disambiguated ID Type / Status
Subject A View to a Kill E90108 entity
Predicate editedBy P1954 FINISHED
Object Peter Davies
Peter Davies is a film editor best known for his work on the James Bond movie "A View to a Kill."
 E398653 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: Peter Davies | Statement: [A View to a Kill, editedBy, Peter Davies]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Peter Davies
Context triple: [A View to a Kill, editedBy, Peter Davies]
  • A. Peter Smillie
    Peter Smillie is a music video director known for his work on high-profile pop and R&B videos in the late 20th century.
  • B. Dave Papworth
    Dave Papworth is a computer engineer best known as one of the founders of the innovative microprocessor company Transmeta.
  • C. David Pegg
    David Pegg was an English footballer who played as a left winger for Manchester United's famed "Busby Babes" before his life was tragically cut short in the Munich air disaster of 1958.
  • D. Stuart Davies
    Stuart Davies was a British aeronautical engineer best known for leading the design of the Avro Vulcan strategic bomber.
  • E. Peter Garnsey
    Peter Garnsey is a prominent historian of the ancient world, particularly known for his influential scholarship on the social, economic, and legal history of the Roman Empire.
  • 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: Peter Davies
Triple: [A View to a Kill, editedBy, Peter Davies]
Generated description
Peter Davies is a film editor best known for his work on the James Bond movie "A View to a Kill."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Peter Davies
Target entity description: Peter Davies is a film editor best known for his work on the James Bond movie "A View to a Kill."
  • A. Peter Smillie
    Peter Smillie is a music video director known for his work on high-profile pop and R&B videos in the late 20th century.
  • B. Dave Papworth
    Dave Papworth is a computer engineer best known as one of the founders of the innovative microprocessor company Transmeta.
  • C. David Pegg
    David Pegg was an English footballer who played as a left winger for Manchester United's famed "Busby Babes" before his life was tragically cut short in the Munich air disaster of 1958.
  • D. Stuart Davies
    Stuart Davies was a British aeronautical engineer best known for leading the design of the Avro Vulcan strategic bomber.
  • E. Peter Garnsey
    Peter Garnsey is a prominent historian of the ancient world, particularly known for his influential scholarship on the social, economic, and legal history of the Roman Empire.
  • 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_69aed95b3c088190a8f85d19e6070599 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeec1ff39c8190b83a88abd840a0e3 completed March 9, 2026, 3:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5283ccf68819086c6349ceb71f099 completed March 14, 2026, 9:19 a.m.
NEDg Description generation batch_69b5295cbab88190900c2d899366d688 completed March 14, 2026, 9:24 a.m.
NED2 Entity disambiguation (via description) batch_69b529c83a5481908e9179553271d23b completed March 14, 2026, 9:26 a.m.
Created at: March 9, 2026, 3:19 p.m.