Triple

T2129675
Position Surface form Disambiguated ID Type / Status
Subject White Chicks E46507 entity
Predicate screenwriter P2831 FINISHED
Object Xavier Cook
Xavier Cook is a screenwriter best known for his work on the comedy film "White Chicks."
E237353 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: Xavier Cook | Statement: [White Chicks, screenwriter, Xavier Cook]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Xavier Cook
Context triple: [White Chicks, screenwriter, Xavier Cook]
  • A. Campbell Dixon
    Campbell Dixon was a British screenwriter active in the early 20th century, known for adapting literary works for the screen.
  • B. Charlie Smith
    Charlie Smith is a fictional protagonist featured as the central character in a narrative work.
  • C. Aaron Ogden
    Aaron Ogden was an early 19th-century American politician and steamboat operator whose state-granted monopoly became the focus of the landmark U.S. Supreme Court case Gibbons v. Ogden, which helped define federal power over interstate commerce.
  • D. Jack Vincennes
    Jack Vincennes is a charismatic, morally conflicted LAPD detective and celebrity cop in the neo-noir crime story "L.A. Confidential."
  • E. Josh Bayliss
    Josh Bayliss is a British business executive best known as the CEO of the Virgin Group, overseeing the conglomerate’s global strategy and operations.
  • 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: Xavier Cook
Triple: [White Chicks, screenwriter, Xavier Cook]
Generated description
Xavier Cook is a screenwriter best known for his work on the comedy film "White Chicks."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Xavier Cook
Target entity description: Xavier Cook is a screenwriter best known for his work on the comedy film "White Chicks."
  • A. Campbell Dixon
    Campbell Dixon was a British screenwriter active in the early 20th century, known for adapting literary works for the screen.
  • B. Charlie Smith
    Charlie Smith is a fictional protagonist featured as the central character in a narrative work.
  • C. Aaron Ogden
    Aaron Ogden was an early 19th-century American politician and steamboat operator whose state-granted monopoly became the focus of the landmark U.S. Supreme Court case Gibbons v. Ogden, which helped define federal power over interstate commerce.
  • D. Jack Vincennes
    Jack Vincennes is a charismatic, morally conflicted LAPD detective and celebrity cop in the neo-noir crime story "L.A. Confidential."
  • E. Josh Bayliss
    Josh Bayliss is a British business executive best known as the CEO of the Virgin Group, overseeing the conglomerate’s global strategy and operations.
  • 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_69a88a1626548190ae59a5028c3baa8e completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abbb77ccc4819087bee5dbb91b5ae8 completed March 7, 2026, 5:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae51a5d95881909b4b77c14f565e21 completed March 9, 2026, 4:50 a.m.
NEDg Description generation batch_69ae523cdebc819088b94e67b5311527 completed March 9, 2026, 4:53 a.m.
NED2 Entity disambiguation (via description) batch_69ae52c56c5c8190bbdd2af3dde63374 completed March 9, 2026, 4:55 a.m.
Created at: March 4, 2026, 7:44 p.m.