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.