Triple
T3572102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bucky Badger |
E75596
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Bucky Badger |
E75596
|
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: Bucky Badger | Statement: [Bucky Badger, name, Bucky Badger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bucky Badger Context triple: [Bucky Badger, name, Bucky Badger]
-
A.
Bucky Badger
chosen
Bucky Badger is the cartoon badger mascot of the University of Wisconsin–Madison, known for representing the school's athletic teams and spirit at sporting events and campus activities.
-
B.
Banzi
Banzi is a town in the Basilicata region of southern Italy, known as the modern site near the ancient Lucanian city of Bantia.
-
C.
Bucky
Bucky is a wiry, fast-talking member of the Junkyard Gang in the animated series "Fat Albert and the Cosby Kids," known for his distinctive buck teeth and energetic personality.
-
D.
Baiju
Baiju was a 13th-century Mongol general who led Mongol forces in their campaigns into Eastern Europe.
-
E.
Badger
Badger is a wise, kind, and somewhat reclusive character from Kenneth Grahame’s "The Wind in the Willows," known for offering guidance and shelter to his woodland friends.
- 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_69ad85d5e3008190bdfe0bacdd1f5a1b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc0c447fc81909689259558187af4 |
completed | March 8, 2026, 6:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3bbbcf2d08190901049948df66f0c |
completed | March 13, 2026, 7:24 a.m. |
Created at: March 8, 2026, 3:21 p.m.