Triple

T8195406
Position Surface form Disambiguated ID Type / Status
Subject John Seely Brown E191417 entity
Predicate givenName P17 FINISHED
Object John
John Seely Brown is an American researcher and former chief scientist at Xerox PARC known for his work on organizational learning, innovation, and the social aspects of technology.
E718413 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 Seely Brown, givenName, John]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John
Context triple: [John Seely Brown, givenName, John]
  • A. John
    John is the given first name of J. Edgar Hoover, the long-serving and influential first director of the United States Federal Bureau of Investigation (FBI).
  • B. John
    John is the given name of the late American comedian and actor John Belushi, famed for his work on "Saturday Night Live" and in films like "Animal House" and "The Blues Brothers."
  • C. John
    John is the given name of the influential American jazz saxophonist and composer John Coltrane.
  • D. John
    John is the given name of John Stevens Henslow, the 19th-century English clergyman, botanist, and mentor to Charles Darwin.
  • E. John
    John is the birth name of American character actor Jack Warden, known for his prolific film and television career in the mid-20th century.
  • 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 Seely Brown, givenName, John]
Generated description
John Seely Brown is an American researcher and former chief scientist at Xerox PARC known for his work on organizational learning, innovation, and the social aspects of technology.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: John
Target entity description: John Seely Brown is an American researcher and former chief scientist at Xerox PARC known for his work on organizational learning, innovation, and the social aspects of technology.
  • A. 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.
  • B. John
    John W. Tukey was an influential American mathematician and statistician known for pioneering exploratory data analysis and coining the term "bit."
  • C. John
    John is the given first name of the influential British computer scientist Robin Milner, known for his pioneering work in programming language theory and process calculi.
  • D. John
    John, known formally as Lord Browne of Madingley, is a prominent British businessman and former chief executive of BP.
  • E. John
    John is the given name of John R. Pierce, an American engineer and scientist known for his pioneering work in communications and satellite technology.
  • 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_69ca82c6e9548190a4c5ca14516e4417 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb5c20fbd08190b9966e3c967e9c71 completed March 31, 2026, 5:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69ccecc7c7f08190981e00342325a6da completed April 1, 2026, 10 a.m.
NEDg Description generation batch_69ccf09b827881908e7fd7e9ff251674 completed April 1, 2026, 10:16 a.m.
NED2 Entity disambiguation (via description) batch_69cd059457788190a900402ee4cd50d5 completed April 1, 2026, 11:46 a.m.
Created at: March 30, 2026, 5:42 p.m.