Triple
T10518836
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dan Bylsma |
E248108
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Disco Dan |
E248108
|
NE FINISHED |
How this triple was built (2 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: Disco Dan | Statement: [Dan Bylsma, nickname, Disco Dan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Disco Dan Context triple: [Dan Bylsma, nickname, Disco Dan]
-
A.
Disco Dan
chosen
Disco Dan is the nickname of Dan Bylsma, a Stanley Cup–winning former NHL head coach best known for his tenure with the Pittsburgh Penguins.
-
B.
Handsome Dan
Handsome Dan is the live bulldog mascot and enduring symbol of Yale University's athletic teams and school spirit.
-
C.
Mr. DJ
Mr. DJ is a hip-hop music producer best known for his work with OutKast and on tracks like "Universal Mind Control."
-
D.
Disko Troop
Disko Troop is the tough, principled Gloucester fishing captain in Rudyard Kipling’s novel "Captains Courageous" who helps transform spoiled rich boy Harvey Cheyne into a responsible young man.
-
E.
Dandy Dan
Dandy Dan is the sharply dressed, ruthless mob boss antagonist in the 1976 musical gangster film "Bugsy Malone."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d381c4aa948190942e1d803143fb0e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d509dd29b48190aa5b170e2558545c |
completed | April 7, 2026, 1:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d90e063e948190b2f7cbae05d9ea61 |
completed | April 10, 2026, 2:49 p.m. |
Created at: April 6, 2026, 12:28 p.m.