Triple

T4706880
Position Surface form Disambiguated ID Type / Status
Subject John F. Clauser E104409 entity
Predicate givenName P17 FINISHED
Object John
John is the given name of John F. Clauser, an American physicist and Nobel laureate known for his pioneering experimental tests of quantum entanglement and Bell's inequalities.
E466027 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 | Statement: [John F. Clauser, givenName, John]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John
Context triple: [John F. Clauser, givenName, John]
  • A. John
    John is the given first name of the legendary American professional golfer Byron Nelson, one of the sport’s early great champions.
  • B. John
    John is the husband of Martha Rainsborough.
  • C. John
    John is the given name of American actor John Goodman, renowned for his roles in film, television, and theater.
  • D. John
    John is the given name of John F. Fitzgerald, an American politician who served as mayor of Boston and was the maternal grandfather of President John F. Kennedy.
  • E. John
    John Guillermin was a British film director and producer best known for directing large-scale adventure and disaster films such as "The Towering Inferno" and the 1976 remake of "King Kong."
  • 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
Triple: [John F. Clauser, givenName, John]
Generated description
John is the given name of John F. Clauser, an American physicist and Nobel laureate known for his pioneering experimental tests of quantum entanglement and Bell's inequalities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: John
Target entity description: John is the given name of John F. Clauser, an American physicist and Nobel laureate known for his pioneering experimental tests of quantum entanglement and Bell's inequalities.
  • A. John
    John is the given name of John Robert Schrieffer, the American physicist and Nobel laureate known for co-developing the BCS theory of superconductivity.
  • B. John
    John is the given name of the influential American theoretical physicist John Archibald Wheeler, known for his work in quantum mechanics and general relativity.
  • C. John
    John is the given name of John Cockcroft, a pioneering British physicist and Nobel laureate known for his work on nuclear physics and particle acceleration.
  • D. John
    John is the given name of John Bardeen, the American physicist who uniquely won the Nobel Prize in Physics twice for his work on the transistor and superconductivity.
  • E. John
    John is the given name of John Polanyi, a Nobel Prize–winning chemist known for his work on chemical kinetics and reaction dynamics.
  • 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_69bd43eac3c08190af7e4020c6c3704c completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd63e9f0b88190820aa7fba2f91b6e completed March 20, 2026, 3:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69be39e6428c81909be9bdb314993b1e completed March 21, 2026, 6:25 a.m.
NEDg Description generation batch_69be3c02014c81908a6f3ed676e5505c completed March 21, 2026, 6:34 a.m.
NED2 Entity disambiguation (via description) batch_69be3cd64d0c8190b007e9f027185225 completed March 21, 2026, 6:38 a.m.
Created at: March 20, 2026, 1:17 p.m.