Triple
T15469885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coffee Town |
E372130
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Ryan Coffey
Ryan Coffey is an actor known for appearing in the comedy film "Coffee Town."
|
E1158715
|
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: Ryan Coffey | Statement: [Coffee Town, castMember, Ryan Coffey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ryan Coffey Context triple: [Coffee Town, castMember, Ryan Coffey]
-
A.
Jeff Cooney
Jeff Cooney is a television producer best known for his executive production work on the biographical anthology series "Genius."
-
B.
Chris Caffery
Chris Caffery is an American guitarist best known for his work with the metal band Savatage and the symphonic rock group Trans-Siberian Orchestra.
-
C.
Chris Coy
Chris Coy is an American actor known for his roles in film and television, including a part in the horror-thriller "Deliver Us from Evil."
-
D.
Timothy Cooney
Timothy Cooney is known primarily as the husband of Joan Ganz Cooney, the television producer and co-creator of Sesame Street.
-
E.
Ben Covington
Ben Covington is a central love interest and college student in the television drama "Felicity," known for his complex, on-and-off relationship with the title character.
- 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: Ryan Coffey Triple: [Coffee Town, castMember, Ryan Coffey]
Generated description
Ryan Coffey is an actor known for appearing in the comedy film "Coffee Town."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ryan Coffey Target entity description: Ryan Coffey is an actor known for appearing in the comedy film "Coffee Town."
-
A.
Jeff Cooney
Jeff Cooney is a television producer best known for his executive production work on the biographical anthology series "Genius."
-
B.
Chris Caffery
Chris Caffery is an American guitarist best known for his work with the metal band Savatage and the symphonic rock group Trans-Siberian Orchestra.
-
C.
Chris Coy
Chris Coy is an American actor known for his roles in film and television, including a part in the horror-thriller "Deliver Us from Evil."
-
D.
Timothy Cooney
Timothy Cooney is known primarily as the husband of Joan Ganz Cooney, the television producer and co-creator of Sesame Street.
-
E.
Ben Covington
Ben Covington is a central love interest and college student in the television drama "Felicity," known for his complex, on-and-off relationship with the title character.
- 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_69d85cc8bd308190886949510b42e764 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f6b49788190b270fdfe92646842 |
completed | April 16, 2026, 1:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff2d03845c8190bc8cb96827a5da39 |
completed | May 9, 2026, 12:48 p.m. |
| NEDg | Description generation | batch_69ff2e2fb3e48190b2274d54586b1f00 |
completed | May 9, 2026, 12:53 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff2ec231dc8190b82333e3e54ced20 |
completed | May 9, 2026, 12:55 p.m. |
Created at: April 10, 2026, 3:33 a.m.