Triple
T16844364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Winner |
E409496
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Matthew Riggs
Matthew Riggs is a fictional character from the television series "The Winner."
|
E1246578
|
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: Matthew Riggs | Statement: [The Winner, character, Matthew Riggs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matthew Riggs Context triple: [The Winner, character, Matthew Riggs]
-
A.
Matthew McNulty
Matthew McNulty is a British actor known for his work in film and television, including roles in series like "Misfits," "The Mill," and "Versailles."
-
B.
Matthew Aldrich
Matthew Aldrich is an American screenwriter best known for co-writing Pixar’s Academy Award–winning animated film "Coco."
-
C.
Chris Ridenhour
Chris Ridenhour is a film composer known for scoring numerous low-budget genre movies, including works produced by The Asylum.
-
D.
Matthew Rutler
Matthew Rutler is an American film production assistant and guitarist best known for his long-term relationship and marriage to singer Christina Aguilera.
-
E.
Matt Rager
Matt Rager is a screenwriter best known for adapting classic American literature, including John Steinbeck’s works, for contemporary film.
- 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: Matthew Riggs Triple: [The Winner, character, Matthew Riggs]
Generated description
Matthew Riggs is a fictional character from the television series "The Winner."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Matthew Riggs Target entity description: Matthew Riggs is a fictional character from the television series "The Winner."
-
A.
Matthew McNulty
Matthew McNulty is a British actor known for his work in film and television, including roles in series like "Misfits," "The Mill," and "Versailles."
-
B.
Matthew Aldrich
Matthew Aldrich is an American screenwriter best known for co-writing Pixar’s Academy Award–winning animated film "Coco."
-
C.
Chris Ridenhour
Chris Ridenhour is a film composer known for scoring numerous low-budget genre movies, including works produced by The Asylum.
-
D.
Matthew Rutler
Matthew Rutler is an American film production assistant and guitarist best known for his long-term relationship and marriage to singer Christina Aguilera.
-
E.
Matt Rager
Matt Rager is a screenwriter best known for adapting classic American literature, including John Steinbeck’s works, for contemporary film.
- 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_69d883952b048190887740a980b712ed |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b3533a9481909730571e6f758cbe |
completed | April 18, 2026, 4:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a011b3802f08190acd5be6e2ccef2d7 |
completed | May 10, 2026, 11:56 p.m. |
| NEDg | Description generation | batch_6a011bf2d25c8190b512de2928550283 |
completed | May 10, 2026, 11:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a011c63308481908b32716eb913b9bd |
completed | May 11, 2026, 12:01 a.m. |
Created at: April 10, 2026, 5:24 a.m.