Triple

T21292424
Position Surface form Disambiguated ID Type / Status
Subject John Litel E524829 entity
Predicate givenName P17 FINISHED
Object John
John Litel was an American character actor known for his prolific work in films and early television from the 1930s through the 1960s.
E1476319 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 Litel, givenName, John]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John
Context triple: [John Litel, givenName, John]
  • A. John
    John is the given name of Saint John of Capistrano, a 15th-century Franciscan friar renowned as a preacher, theologian, and leader in the defense of Belgrade.
  • B. John
    John B. Magruder was a Confederate major general during the American Civil War, known for his leadership in the Peninsula Campaign and his flamboyant personality.
  • C. John
    John is the given name of John Bercow, a British politician who served as Speaker of the House of Commons in the UK Parliament.
  • D. John
    John is the given name of the English Romantic poet and nobleman John Byron, 7th Baron Byron.
  • E. John
    John Sirica was a U.S. federal judge best known for presiding over the Watergate scandal trials in the 1970s.
  • 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 Litel, givenName, John]
Generated description
John Litel was an American character actor known for his prolific work in films and early television from the 1930s through the 1960s.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: John
Target entity description: John Litel was an American character actor known for his prolific work in films and early television from the 1930s through the 1960s.
  • A. 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.
  • B. 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.
  • C. John
    John is the birth name of American actor and director Jackie Cooper, a prominent child star of the early 20th century.
  • D. John
    John is the birth name of American actor Jack Lemmon, a celebrated star of classic films such as "Some Like It Hot" and "The Apartment."
  • E. John
    John is the first name of American actor J. D. Cannon, known for his roles in film and television during the mid-20th century.
  • 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_69e0b517e6748190850d6f6ddf323d69 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e73855e5d08190aed5e285247b4e23 completed April 21, 2026, 8:41 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0997f6a5908190958598492a1363e8 completed May 17, 2026, 10:27 a.m.
NEDg Description generation batch_6a0998a3326481908a4dfddfb7b2ecd9 completed May 17, 2026, 10:29 a.m.
NED2 Entity disambiguation (via description) batch_6a09996c8d7c81908cc9874f6e1a3fff completed May 17, 2026, 10:33 a.m.
Created at: April 16, 2026, 4:04 p.m.