Triple

T2547239
Position Surface form Disambiguated ID Type / Status
Subject The Bells of San Angelo E57932 entity
Predicate starring P1507 FINISHED
Object John McGuire
John McGuire was an American film actor active in the 1930s and 1940s, known for his roles in crime dramas and Westerns.
E279865 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: John McGuire | Statement: [The Bells of San Angelo, starring, John McGuire]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John McGuire
Context triple: [The Bells of San Angelo, starring, John McGuire]
  • A. Ray Danton
    Ray Danton was an American actor and director best known for his suave, often villainous roles in film and television during the 1950s and 1960s.
  • B. Jack Oaker
    Jack Oaker was the husband of silent film actress Belle Bennett, known primarily in relation to her life and career.
  • C. Dan Hampton
    Dan Hampton is a Hall of Fame defensive lineman best known for anchoring the Chicago Bears’ dominant 1980s defense.
  • D. Ralph Boston
    Ralph Boston was an American track and field athlete best known for breaking the long jump world record and winning the gold medal at the 1960 Rome Olympics.
  • E. Bill Cobbs
    Bill Cobbs is an American character actor known for his prolific supporting roles in film and television, including appearances in movies like "Night at the Museum," "Demolition Man," and "The Hudsucker Proxy."
  • 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: John McGuire
Triple: [The Bells of San Angelo, starring, John McGuire]
Generated description
John McGuire was an American film actor active in the 1930s and 1940s, known for his roles in crime dramas and Westerns.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: John McGuire
Target entity description: John McGuire was an American film actor active in the 1930s and 1940s, known for his roles in crime dramas and Westerns.
  • A. Ray Danton
    Ray Danton was an American actor and director best known for his suave, often villainous roles in film and television during the 1950s and 1960s.
  • B. Jack Oaker
    Jack Oaker was the husband of silent film actress Belle Bennett, known primarily in relation to her life and career.
  • C. Dan Hampton
    Dan Hampton is a Hall of Fame defensive lineman best known for anchoring the Chicago Bears’ dominant 1980s defense.
  • D. Ralph Boston
    Ralph Boston was an American track and field athlete best known for breaking the long jump world record and winning the gold medal at the 1960 Rome Olympics.
  • E. Bill Cobbs
    Bill Cobbs is an American character actor known for his prolific supporting roles in film and television, including appearances in movies like "Night at the Museum," "Demolition Man," and "The Hudsucker Proxy."
  • 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_69ab4a5212d88190b989ce129f2ad87f completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd2e672948190bb7fe9b47535a172 completed March 7, 2026, 7:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69af655a0f088190a7ac30c6df1fdff6 completed March 10, 2026, 12:27 a.m.
NEDg Description generation batch_69af669436208190901d1f34592c1a42 completed March 10, 2026, 12:32 a.m.
NED2 Entity disambiguation (via description) batch_69af6760cf7c8190bb681f573828049e completed March 10, 2026, 12:35 a.m.
Created at: March 6, 2026, 9:47 p.m.