Triple

T18797693
Position Surface form Disambiguated ID Type / Status
Subject FIA Hall of Fame E459678 entity
Predicate notableInductee P7102 FINISHED
Object Tom Kristensen
Tom Kristensen is a legendary Danish racing driver best known for his record number of victories at the 24 Hours of Le Mans.
 E1342773 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: Tom Kristensen | Statement: [FIA Hall of Fame, notableInductee, Tom Kristensen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tom Kristensen
Context triple: [FIA Hall of Fame, notableInductee, Tom Kristensen]
  • A. Jørgen Rasmussen
    Jørgen Rasmussen is a Danish former footballer and manager known for his contributions to Danish club football in the mid-20th century.
  • B. Niki Rüttimann
    Niki Rüttimann is a Swiss former professional road cyclist known for his successes in major European stage races during the 1980s.
  • C. Mark Donohue
    Mark Donohue is a linguist known for his descriptive and analytical work on Austronesian and Papuan languages, including the Tukang Besi language.
  • D. Ken Miles
    Ken Miles was a British-born racing driver and engineer best known for his pivotal role in Ford’s 1960s Le Mans program and his portrayal in the film "Ford v Ferrari."
  • E. Bjørn Magnussen
    Bjørn Magnussen is a Norwegian speed skater who competes internationally, particularly in sprint and team events.
  • 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: Tom Kristensen
Triple: [FIA Hall of Fame, notableInductee, Tom Kristensen]
Generated description
Tom Kristensen is a legendary Danish racing driver best known for his record number of victories at the 24 Hours of Le Mans.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tom Kristensen
Target entity description: Tom Kristensen is a legendary Danish racing driver best known for his record number of victories at the 24 Hours of Le Mans.
  • A. Jørgen Rasmussen
    Jørgen Rasmussen is a Danish former footballer and manager known for his contributions to Danish club football in the mid-20th century.
  • B. Niki Rüttimann
    Niki Rüttimann is a Swiss former professional road cyclist known for his successes in major European stage races during the 1980s.
  • C. Mark Donohue
    Mark Donohue is a linguist known for his descriptive and analytical work on Austronesian and Papuan languages, including the Tukang Besi language.
  • D. Ken Miles
    Ken Miles was a British-born racing driver and engineer best known for his pivotal role in Ford’s 1960s Le Mans program and his portrayal in the film "Ford v Ferrari."
  • E. Bjørn Magnussen
    Bjørn Magnussen is a Norwegian speed skater who competes internationally, particularly in sprint and team events.
  • 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_69d8d398c7d4819091cb2f7e48948aeb completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5a020821881909749f6a1c6cd195b completed April 20, 2026, 3:40 a.m.
NED1 Entity disambiguation (via context triple) batch_6a05472244488190be57a1bb7d1be5a1 completed May 14, 2026, 3:53 a.m.
NEDg Description generation batch_6a054859b270819081912c2110d30263 completed May 14, 2026, 3:58 a.m.
NED2 Entity disambiguation (via description) batch_6a0548f1ed74819090775763f4976a1a completed May 14, 2026, 4 a.m.
Created at: April 10, 2026, 11:53 a.m.