Triple

T15435923
Position Surface form Disambiguated ID Type / Status
Subject Dr. John Becker E369761 entity
Predicate givenName P17 FINISHED
Object John
John is the given name of the fictional character Dr. John Becker, a cynical Bronx doctor from the American television sitcom "Becker."
E1157441 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: [Dr. John Becker, givenName, John]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John
Context triple: [Dr. John Becker, givenName, John]
  • A. John
    John is the given name of John W. Mauchly, the American physicist and co-inventor of the ENIAC computer.
  • B. John
    John is the given first name of Johnny Kilbane, an American featherweight boxing champion from the early 20th century.
  • C. John
    John is the given name of John Albert William Spencer-Churchill, a British aristocrat and 10th Duke of Marlborough.
  • D. John
    John is the given first name of the 19th-century English theologian and social reformer Frederick Denison Maurice.
  • E. John
    John is the given name of Sir John Lennard-Jones, a pioneering British theoretical chemist known for his work on intermolecular forces and the Lennard-Jones potential.
  • 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: [Dr. John Becker, givenName, John]
Generated description
John is the given name of the fictional character Dr. John Becker, a cynical Bronx doctor from the American television sitcom "Becker."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: John
Target entity description: John is the given name of the fictional character Dr. John Becker, a cynical Bronx doctor from the American television sitcom "Becker."
  • A. John
    John is the given name of the fictional character Trapper John McIntyre from the M*A*S*H franchise.
  • B. John
    John is the given name of American character actor John McGiver, known for his distinctive, pompous persona in mid-20th-century film and television.
  • C. John
    John is the given name of John Bodkin Adams, a British physician notorious for being suspected of murdering numerous patients in the mid-20th century.
  • D. John
    John is the first name of the fictional character John Connor, the prophesied leader of the human resistance in the Terminator franchise.
  • E. John
    John is a fictional police detective and main character from the science fiction TV series "Almost Human."
  • 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_69d85a19180081909925012fbf4e62a3 completed April 10, 2026, 2:02 a.m.
NER Named-entity recognition batch_69e03edb3ec481908b26164d4470c9bc completed April 16, 2026, 1:43 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff21a3d1f481908f6795656514b2b4 completed May 9, 2026, 11:59 a.m.
NEDg Description generation batch_69ff2299ac9481909ad213ff5bb01db3 completed May 9, 2026, 12:03 p.m.
NED2 Entity disambiguation (via description) batch_69ff2325e6a48190bee256ef8720ba8e completed May 9, 2026, 12:05 p.m.
Created at: April 10, 2026, 3:21 a.m.