Triple

T14429150
Position Surface form Disambiguated ID Type / Status
Subject John F. Seitz E357775 entity
Predicate givenName P17 FINISHED
Object John
John is the given name of American cinematographer John F. Seitz, known for his influential work in early Hollywood cinema.
E1098554 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 F. Seitz, givenName, John]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John
Context triple: [John F. Seitz, givenName, John]
  • A. John
    John is the given first name of Johnny Kilbane, an American featherweight boxing champion from the early 20th century.
  • B. John
    John is the middle name of Samuel John Mills, an American Congregationalist minister known for his role in early 19th-century missionary movements.
  • C. John
    John is the given name of John C. Sheehan, an American organic chemist renowned for achieving the first complete laboratory synthesis of penicillin.
  • D. John
    John was a Portuguese royal who held the title of Prince of Brazil and later became King John VI of Portugal.
  • E. John
    John is the given name of John Boyle O'Reilly, a 19th-century Irish-born poet, journalist, and civil rights activist who became influential in the United States.
  • 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 F. Seitz, givenName, John]
Generated description
John is the given name of American cinematographer John F. Seitz, known for his influential work in early Hollywood cinema.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: John
Target entity description: John is the given name of American cinematographer John F. Seitz, known for his influential work in early Hollywood cinema.
  • A. John
    John is the given name of Australian cinematographer John Seale, known for his work on films such as "The English Patient" and "Mad Max: Fury Road."
  • B. John
    John is the given name of American filmmaker John Hughes, known for his influential 1980s teen comedies and coming-of-age films.
  • C. John
    John is the given name of American film director John Sturges, known for classic Westerns and action films such as "The Magnificent Seven" and "The Great Escape."
  • D. John
    John is the first name of American filmmaker John Lee Hancock, known for directing and writing several popular Hollywood films.
  • E. John
    John is the first name of John Ottman, an American film editor and composer known for his work on major Hollywood movies.
  • 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_69d8279402a88190821ffa39ae15bccf completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de91154de881909266ae88d1545685 completed April 14, 2026, 7:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd5bc424f88190ab3a1c1aec61cb40 completed May 8, 2026, 3:43 a.m.
NEDg Description generation batch_69fd5cf4dedc81908988f13f0fc9f510 completed May 8, 2026, 3:48 a.m.
NED2 Entity disambiguation (via description) batch_69fd5dcf151c8190959b3240813a1d71 completed May 8, 2026, 3:51 a.m.
Created at: April 10, 2026, 1:18 a.m.