Triple

T17961810
Position Surface form Disambiguated ID Type / Status
Subject John Kitzmiller E449101 entity
Predicate givenName P17 FINISHED
Object John
John is the given name of John Kitzmiller, an American actor best known for his roles in mid-20th-century European cinema, including the James Bond film "Dr. No."
E1298878 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 Kitzmiller, givenName, John]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John
Context triple: [John Kitzmiller, givenName, John]
  • A. John
    John is the given first name of the legendary American professional golfer Byron Nelson, one of the sport’s early great champions.
  • 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 Buffalo Mailer is an American writer, actor, and producer known for his work in theater, film, and political commentary.
  • E. John
    John is the middle name of the 19th-century English naturalist and illustrator William John Swainson.
  • 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 Kitzmiller, givenName, John]
Generated description
John is the given name of John Kitzmiller, an American actor best known for his roles in mid-20th-century European cinema, including the James Bond film "Dr. No."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: John
Target entity description: John is the given name of John Kitzmiller, an American actor best known for his roles in mid-20th-century European cinema, including the James Bond film "Dr. No."
  • A. John
    John is the given name of American actor John Phillip Law, known for his roles in films such as "Barbarella" and "The Golden Voyage of Sinbad."
  • B. John
    John is the given name of American actor John Dall, known for his roles in classic films such as "Rope" and "Gun Crazy."
  • C. John
    John is the given name of American actor John Hodiak, known for his roles in mid-20th-century Hollywood films.
  • D. John
    John is the given name of American actor John Miljan, who was known for his prolific work in early Hollywood films, often portraying suave villains and authority figures.
  • E. John
    John is the given name of American film and television director John Frankenheimer, known for works like "The Manchurian Candidate" and "Ronin."
  • 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_69d8b9f8cca8819099836916c56b7c95 completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e4b132cc10819088526a0b4b098d69 completed April 19, 2026, 10:40 a.m.
NED1 Entity disambiguation (via context triple) batch_6a033000790c8190b65c0f888bdd9482 completed May 12, 2026, 1:49 p.m.
NEDg Description generation batch_6a0331bd44f08190b564dae3fee6dcbd completed May 12, 2026, 1:57 p.m.
NED2 Entity disambiguation (via description) batch_6a03342d68e08190901e2fcb33627714 completed May 12, 2026, 2:07 p.m.
Created at: April 10, 2026, 10:22 a.m.