Triple
T3342251
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coach |
E70285
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Dauber Dybinski
Dauber Dybinski is a dim-witted but good-hearted assistant coach character from the American sitcom "Coach."
|
E349950
|
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: Dauber Dybinski | Statement: [Coach, character, Dauber Dybinski]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dauber Dybinski Context triple: [Coach, character, Dauber Dybinski]
-
A.
Daniel Dubiecki
Daniel Dubiecki is an American film producer known for his work on acclaimed movies such as "Up in the Air" and other high-profile Hollywood projects.
-
B.
Adam Bielecki
Adam Bielecki is a Polish high-altitude mountaineer renowned for pioneering bold winter ascents in the Himalayas and Karakoram.
-
C.
John Wolyniec
John Wolyniec is a former American professional soccer forward best known for his time with the New York/New Jersey MetroStars and New York Red Bulls in Major League Soccer.
-
D.
Andrew Goczkowski
Andrew Goczkowski is an American local government leader serving as the mayor of Des Plaines, Illinois.
-
E.
Leon Wasilewski
Leon Wasilewski was a Polish politician, diplomat, and historian known for serving as Poland’s first foreign minister after World War I and for his work on Polish-Ukrainian relations.
- 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: Dauber Dybinski Triple: [Coach, character, Dauber Dybinski]
Generated description
Dauber Dybinski is a dim-witted but good-hearted assistant coach character from the American sitcom "Coach."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dauber Dybinski Target entity description: Dauber Dybinski is a dim-witted but good-hearted assistant coach character from the American sitcom "Coach."
-
A.
Daniel Dubiecki
Daniel Dubiecki is an American film producer known for his work on acclaimed movies such as "Up in the Air" and other high-profile Hollywood projects.
-
B.
Adam Bielecki
Adam Bielecki is a Polish high-altitude mountaineer renowned for pioneering bold winter ascents in the Himalayas and Karakoram.
-
C.
John Wolyniec
John Wolyniec is a former American professional soccer forward best known for his time with the New York/New Jersey MetroStars and New York Red Bulls in Major League Soccer.
-
D.
Andrew Goczkowski
Andrew Goczkowski is an American local government leader serving as the mayor of Des Plaines, Illinois.
-
E.
Leon Wasilewski
Leon Wasilewski was a Polish politician, diplomat, and historian known for serving as Poland’s first foreign minister after World War I and for his work on Polish-Ukrainian relations.
- 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_69ad85a405e48190b6e68de7cf9f319e |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb1ee711481909c0d921f1b5b8562 |
completed | March 8, 2026, 5:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b31a946ba88190bd40ba7481baf28d |
completed | March 12, 2026, 7:57 p.m. |
| NEDg | Description generation | batch_69b31c3aaba48190b203e344d71080f3 |
completed | March 12, 2026, 8:04 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b31da28a04819096e7ced5f123592a |
completed | March 12, 2026, 8:10 p.m. |
Created at: March 8, 2026, 3:12 p.m.