Triple

T14910782
Position Surface form Disambiguated ID Type / Status
Subject John W. Tukey E371254 entity
Predicate givenName P17 FINISHED
Object John
John W. Tukey was an influential American mathematician and statistician known for pioneering exploratory data analysis and coining terms like "bit" and "software."
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 is the middle name of Samuel John Mills, an American Congregationalist minister known for his role in early 19th-century missionary movements.
  • C. John
    John is the given name of John C. Sheehan, an American organic chemist renowned for achieving the first complete laboratory synthesis of penicillin.
  • D. John
    John was a Portuguese royal who held the title of Prince of Brazil and later became King John VI of Portugal.
  • E. John
    John is the given name of John Boyle O'Reilly, a 19th-century Irish-born poet, journalist, and civil rights activist who became influential in the United States.
  • 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 W. Tukey was an influential American mathematician and statistician known for pioneering exploratory data analysis and coining terms like "bit" and "software."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: John
Target entity description: John W. Tukey was an influential American mathematician and statistician known for pioneering exploratory data analysis and coining terms like "bit" and "software."
  • A. John chosen
    John W. Tukey was an influential American mathematician and statistician known for pioneering exploratory data analysis and coining the term "bit."
  • B. John
    John G. Kemeny was a Hungarian-American mathematician and computer scientist best known as the co-inventor of the BASIC programming language and former president of Dartmouth College.
  • C. John
    John is the given name of the American mathematician John Tate, renowned for his foundational contributions to number theory and arithmetic geometry.
  • 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
    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.
  • 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_69d85cc7ea3481908228b5acb7d06f12 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded61c6b9c8190a92934d49b98fe46 completed April 15, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe72a87dd88190b0f0c3b9b625a7e6 completed May 8, 2026, 11:32 p.m.
NEDg Description generation batch_69fe7360c11481908e2e5127b466e31b completed May 8, 2026, 11:36 p.m.
NED2 Entity disambiguation (via description) batch_69fe743c37308190a045ef5f0ade8508 completed May 8, 2026, 11:39 p.m.
Created at: April 10, 2026, 2:26 a.m.