Triple

T7593231
Position Surface form Disambiguated ID Type / Status
Subject Condon E179790 entity
Predicate hasNotableBearer P458 FINISHED
Object John Condon
John Condon is a relatively common personal name shared by multiple individuals, including figures in fields such as sports, the military, and public service.
 E676691 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 Condon | Statement: [Condon, hasNotableBearer, John Condon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: John Condon
Context triple: [Condon, hasNotableBearer, John Condon]
  • A. John McDonough
    John McDonough was an American football official best known for serving as the referee in Super Bowl IV.
  • B. Michael Condrey
    Michael Condrey is a video game developer best known as the co-founder of Sledgehammer Games and for his work on the Call of Duty franchise.
  • C. Steve Condos
    Steve Condos was an influential American tap dancer renowned for his virtuosic footwork and contributions to rhythm tap.
  • D. John Dolman
    John Dolman was an English clergyman and benefactor of the late 16th century best known for establishing Pocklington School in Yorkshire.
  • E. Mark Sanger
    Mark Sanger is a British film editor best known for his Academy Award–winning work on the science fiction thriller "Gravity."
  • 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 Condon
Triple: [Condon, hasNotableBearer, John Condon]
Generated description
John Condon is a relatively common personal name shared by multiple individuals, including figures in fields such as sports, the military, and public service.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: John Condon
Target entity description: John Condon is a relatively common personal name shared by multiple individuals, including figures in fields such as sports, the military, and public service.
  • A. John McDonough
    John McDonough was an American football official best known for serving as the referee in Super Bowl IV.
  • B. Michael Condrey
    Michael Condrey is a video game developer best known as the co-founder of Sledgehammer Games and for his work on the Call of Duty franchise.
  • C. Steve Condos
    Steve Condos was an influential American tap dancer renowned for his virtuosic footwork and contributions to rhythm tap.
  • D. John Dolman
    John Dolman was an English clergyman and benefactor of the late 16th century best known for establishing Pocklington School in Yorkshire.
  • E. Mark Sanger
    Mark Sanger is a British film editor best known for his Academy Award–winning work on the science fiction thriller "Gravity."
  • 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_69c69f3487ec8190bf7acdf2dd91e6d6 completed March 27, 2026, 3:16 p.m.
NER Named-entity recognition batch_69c6f9bab3a08190a2c36b2c72a1de25 completed March 27, 2026, 9:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69c86843a7808190a4c1d3c33a7441ed completed March 28, 2026, 11:46 p.m.
NEDg Description generation batch_69c869dd249c81908ffa28d301ec5882 completed March 28, 2026, 11:53 p.m.
NED2 Entity disambiguation (via description) batch_69c86a1f1bfc8190b25597a030613e08 completed March 28, 2026, 11:54 p.m.
Created at: March 27, 2026, 3:53 p.m.