Triple

T1790804
Position Surface form Disambiguated ID Type / Status
Subject Ford v Ferrari E39489 entity
Predicate editedBy P1954 FINISHED
Object Andrew Buckland
Andrew Buckland is a film editor best known for his Academy Award-winning work on the racing drama "Ford v Ferrari."
E235506 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: Andrew Buckland | Statement: [Ford v Ferrari, editedBy, Andrew Buckland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Andrew Buckland
Context triple: [Ford v Ferrari, editedBy, Andrew Buckland]
  • A. Richard Bristow
    Richard Bristow was a 16th-century English Catholic scholar and theologian who contributed to the development and annotation of the Douay–Rheims Bible.
  • B. Daniel Butterfield
    Daniel Butterfield was a Union Army general in the American Civil War, best known for composing the bugle call "Taps."
  • C. Geoffrey Smith
    Geoffrey Smith is an Australian Anglican archbishop who serves as the national leader (Primate) of the Anglican Church of Australia.
  • D. Andrew Darwin
    Andrew Darwin is an individual notable for bearing the Darwin surname, historically associated with the famous naturalist Charles Darwin.
  • E. Richard Hiscott
    Richard Hiscott is an editor known for his work on the television series "Willow."
  • 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: Andrew Buckland
Triple: [Ford v Ferrari, editedBy, Andrew Buckland]
Generated description
Andrew Buckland is a film editor best known for his Academy Award-winning work on the racing drama "Ford v Ferrari."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Andrew Buckland
Target entity description: Andrew Buckland is a film editor best known for his Academy Award-winning work on the racing drama "Ford v Ferrari."
  • A. Richard Bristow
    Richard Bristow was a 16th-century English Catholic scholar and theologian who contributed to the development and annotation of the Douay–Rheims Bible.
  • B. Daniel Butterfield
    Daniel Butterfield was a Union Army general in the American Civil War, best known for composing the bugle call "Taps."
  • C. Geoffrey Smith
    Geoffrey Smith is an Australian Anglican archbishop who serves as the national leader (Primate) of the Anglican Church of Australia.
  • D. Andrew Darwin
    Andrew Darwin is an individual notable for bearing the Darwin surname, historically associated with the famous naturalist Charles Darwin.
  • E. Richard Hiscott
    Richard Hiscott is an editor known for his work on the television series "Willow."
  • 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_69a88631854081909723959921e45c2b completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa6512804c8190a5743c10bd37f83f completed March 6, 2026, 5:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae303ee60c819093a70e0e6a431c01 completed March 9, 2026, 2:28 a.m.
NEDg Description generation batch_69ae3418924081909556938e4628ba08 completed March 9, 2026, 2:44 a.m.
NED2 Entity disambiguation (via description) batch_69ae3495fe348190a8dde305ba1df046 completed March 9, 2026, 2:46 a.m.
Created at: March 4, 2026, 7:32 p.m.