Triple
T7595359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doom Patrol |
E179843
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object |
Matthew Zuk
Matthew Zuk is an American actor best known for his role as the physical performer for Negative Man in the television series "Doom Patrol."
|
E674811
|
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 Zuk | Statement: [Doom Patrol, stars, Matthew Zuk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matthew Zuk Context triple: [Doom Patrol, stars, Matthew Zuk]
-
A.
Matthew Freund
Matthew Freund is a film editor known for his work on the comedy movie "Fist Fight."
-
B.
Jonathan Teplitzky
Jonathan Teplitzky is an Australian film director known for character-driven dramas such as "The Railway Man" and "Burning Man."
-
C.
Matthew Arkin
Matthew Arkin is an American actor and acting teacher, known for his work in film, television, and theater and as part of the Arkin family of performers.
-
D.
Matthew Shafer
Matthew Shafer is an American writer known for his work on the animated series "Cowboy Bebop" and related projects.
-
E.
Matthew Shafer
Matthew Shafer, better known by his stage name Uncle Kracker, is an American singer-songwriter and musician recognized for his blend of rock, country, and pop influences.
- 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 Zuk Triple: [Doom Patrol, stars, Matthew Zuk]
Generated description
Matthew Zuk is an American actor best known for his role as the physical performer for Negative Man in the television series "Doom Patrol."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Matthew Zuk Target entity description: Matthew Zuk is an American actor best known for his role as the physical performer for Negative Man in the television series "Doom Patrol."
-
A.
Matthew Freund
Matthew Freund is a film editor known for his work on the comedy movie "Fist Fight."
-
B.
Jonathan Teplitzky
Jonathan Teplitzky is an Australian film director known for character-driven dramas such as "The Railway Man" and "Burning Man."
-
C.
Matthew Arkin
Matthew Arkin is an American actor and acting teacher, known for his work in film, television, and theater and as part of the Arkin family of performers.
-
D.
Matthew Shafer
Matthew Shafer is an American writer known for his work on the animated series "Cowboy Bebop" and related projects.
-
E.
Matthew Shafer
Matthew Shafer, better known by his stage name Uncle Kracker, is an American singer-songwriter and musician recognized for his blend of rock, country, and pop influences.
- 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_69c69f3487ec8190bf7acdf2dd91e6d6 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9bbcd8081909a229d7faa2ffdc8 |
completed | March 27, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8619d6f2081908c8b589d4106691f |
completed | March 28, 2026, 11:17 p.m. |
| NEDg | Description generation | batch_69c86211e4f88190b38bce6441e33b53 |
completed | March 28, 2026, 11:19 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c862bb95e881909a60608a5279238d |
completed | March 28, 2026, 11:22 p.m. |
Created at: March 27, 2026, 3:53 p.m.