Triple

T14975270
Position Surface form Disambiguated ID Type / Status
Subject John W. Tukey E373429 entity
Predicate givenName P17 FINISHED
Object John
John is the given name of the influential American statistician John W. Tukey, known for major contributions to statistics and data analysis.
E373429 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 W. Tukey, givenName, John]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John
Context triple: [John W. Tukey, givenName, John]
  • A. John
    John is the given first name of Johnny Kilbane, an American featherweight boxing champion from the early 20th century.
  • B. John
    John Ross is a personal name shared by various notable individuals across history, including leaders, politicians, and public figures.
  • C. John
    John is the given name of John Adams, the second president of the United States and a prominent Founding Father.
  • D. John
    John is the given name of John C. Sheehan, an American organic chemist renowned for achieving the first complete laboratory synthesis of penicillin.
  • E. John
    John is the given name of John Bowen, a British novelist and playwright known for his crime and speculative fiction.
  • 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 W. Tukey, givenName, John]
Generated description
John is the given name of the influential American statistician John W. Tukey, known for major contributions to statistics and data analysis.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: John
Target entity description: John is the given name of the influential American statistician John W. Tukey, known for major contributions to statistics and data analysis.
  • A. John
    John is the given name of Sir John Kingman, a prominent British mathematician and statistician known for his work in probability theory and population genetics.
  • B. John
    John is the given name of the American mathematician John Tate, renowned for his foundational contributions to number theory and arithmetic geometry.
  • C. John
    John is the given name of the influential American economist John Bates Clark, known for his work on marginal productivity theory.
  • D. John
    John is the given name of John McCarthy, the American computer scientist who coined the term "artificial intelligence" and was a pioneer in the field.
  • E. John chosen
    John W. Tukey was an influential American mathematician and statistician known for pioneering exploratory data analysis and coining the term "bit."
  • F. None of above.

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_69d85ccbbcd48190acb56e7cf104d8ad completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded6e8733081908e06b53746eb6eb6 completed April 15, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe968f25e08190bfbf7a3541f79add completed May 9, 2026, 2:06 a.m.
NEDg Description generation batch_69fe97eccdf081909a94e214900ea0c2 completed May 9, 2026, 2:11 a.m.
NED2 Entity disambiguation (via description) batch_69fe9882aeac819095d432079b8268c5 completed May 9, 2026, 2:14 a.m.
Created at: April 10, 2026, 2:51 a.m.