Triple

T21790763
Position Surface form Disambiguated ID Type / Status
Subject John LeConte E537963 entity
Predicate givenName P17 FINISHED
Object John
John LeConte was a 19th-century American physicist and academic who served as president of the University of California.
E1501896 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 LeConte, givenName, John]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John
Context triple: [John LeConte, givenName, John]
  • A. John
    John Vassall Jr. was a British civil servant who became notorious as a Soviet spy during the Cold War.
  • B. John
    John is the given name of John A. Roebling II, an American civil engineer and philanthropist from the prominent Roebling family associated with major bridge construction.
  • C. John
    John is the given first name of Johnny Kilbane, an American featherweight boxing champion from the early 20th century.
  • D. John
    John is the given name of John Albert William Spencer-Churchill, a British aristocrat and 10th Duke of Marlborough.
  • E. John
    John is the given name of John George Graves, a notable British mail-order entrepreneur and philanthropist from Sheffield.
  • 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 LeConte, givenName, John]
Generated description
John LeConte was a 19th-century American physicist and academic who served as president of the University of California.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: John
Target entity description: John LeConte was a 19th-century American physicist and academic who served as president of the University of California.
  • A. John
    John is the given name of John Milton Gregory, a 19th-century American educator and university president known for helping to found the University of Illinois.
  • B. John
    John C. Calhoun was a prominent 19th-century American statesman and political theorist who served as U.S. vice president, senator, and a leading proponent of states’ rights and slavery.
  • C. John
    John L. Hennessy is an American computer scientist and academic leader best known as a pioneer of RISC architecture and as the former president of Stanford University.
  • D. John
    John is the given name of John Merle Coulter, an American botanist known for his influential work in plant taxonomy and education.
  • E. John
    John is the given name of John Galen Howard, a prominent American architect known for his influential work on the University of California, Berkeley campus.
  • 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_69e0c47198f881908cb0d237266c10e9 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69f0621f95cc8190a3e8619fbf29555a completed April 28, 2026, 7:30 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0a3e68562c8190b60115937a329557 completed May 17, 2026, 10:17 p.m.
NEDg Description generation batch_6a0a3f5813a081909f253f04ca218559 completed May 17, 2026, 10:21 p.m.
NED2 Entity disambiguation (via description) batch_6a0a400831d881909631e1accd5618f1 completed May 17, 2026, 10:24 p.m.
Created at: April 16, 2026, 6:52 p.m.