Triple

T1438015
Position Surface form Disambiguated ID Type / Status
Subject All the President’s Men E31000 entity
Predicate musicBy P1952 FINISHED
Object David Shire
David Shire is an American composer best known for his film and television scores, including acclaimed work in the 1970s.
 E166298 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: David Shire | Statement: [All the President’s Men, musicBy, David Shire]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: David Shire
Context triple: [All the President’s Men, musicBy, David Shire]
  • A. Richard Shepherd
    Richard Shepherd was an American film producer best known for his work on classic movies such as "Breakfast at Tiffany's."
  • B. Robert Mann
    Robert Mann was a 19th-century American man best known as the son of influential education reformer Horace Mann.
  • C. Bill Eyre
    Bill Eyre was an early British aviation figure best known as a co-founder of the Hawker Aircraft company, a major producer of military aircraft in the 20th century.
  • D. Andrew Lesnie
    Andrew Lesnie was an Australian cinematographer best known for his Oscar-winning work on Peter Jackson’s The Lord of the Rings film trilogy.
  • E. Dario Marianelli
    Dario Marianelli is an Italian film composer known for his evocative scores for movies such as Atonement, Pride & Prejudice, and Darkest Hour.
  • 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: David Shire
Triple: [All the President’s Men, musicBy, David Shire]
Generated description
David Shire is an American composer best known for his film and television scores, including acclaimed work in the 1970s.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: David Shire
Target entity description: David Shire is an American composer best known for his film and television scores, including acclaimed work in the 1970s.
  • A. Richard Shepherd
    Richard Shepherd was an American film producer best known for his work on classic movies such as "Breakfast at Tiffany's."
  • B. Robert Mann
    Robert Mann was a 19th-century American man best known as the son of influential education reformer Horace Mann.
  • C. Bill Eyre
    Bill Eyre was an early British aviation figure best known as a co-founder of the Hawker Aircraft company, a major producer of military aircraft in the 20th century.
  • D. Andrew Lesnie
    Andrew Lesnie was an Australian cinematographer best known for his Oscar-winning work on Peter Jackson’s The Lord of the Rings film trilogy.
  • E. Dario Marianelli
    Dario Marianelli is an Italian film composer known for his evocative scores for movies such as Atonement, Pride & Prejudice, and Darkest Hour.
  • 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_69a4991633388190a4d61b5a98aa407a completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c5059ef88190af20e796acdb2058 completed March 1, 2026, 11 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad08ba2cf88190a859bb0974761968 completed March 8, 2026, 5:27 a.m.
NEDg Description generation batch_69ad0bc943488190892a88f4c0e392b9 completed March 8, 2026, 5:40 a.m.
NED2 Entity disambiguation (via description) batch_69ad0c6abed48190815252fabc992c48 completed March 8, 2026, 5:43 a.m.
Created at: March 1, 2026, 8 p.m.