Triple
T11387242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeff Bergman |
E269741
|
entity |
| Predicate | voiceOf |
P2181
|
FINISHED |
| Object | Yosemite Sam |
E57131
|
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: Yosemite Sam | Statement: [Jeff Bergman, voiceOf, Yosemite Sam]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yosemite Sam Context triple: [Jeff Bergman, voiceOf, Yosemite Sam]
-
A.
Yosemite Sam
chosen
Yosemite Sam is a hot-tempered, mustachioed outlaw and recurring antagonist in the Looney Tunes cartoons, known for his fiery personality and frequent clashes with Bugs Bunny.
-
B.
Elmer Fudd
Elmer Fudd is a classic Looney Tunes cartoon character best known as the bumbling, soft-spoken hunter perpetually chasing Bugs Bunny.
-
C.
Charlie Coyote
Charlie Coyote is the costumed athletic mascot representing the University of South Dakota’s sports teams.
-
D.
Cliff Hare
Cliff Hare was an influential Auburn University figure and longtime trustee whose contributions to the school's athletics led to his name being honored on Jordan–Hare Stadium.
-
E.
Wile E. Coyote
Wile E. Coyote is a classic Looney Tunes cartoon character best known as the endlessly scheming, perpetually failing predator obsessed with catching the Road Runner.
- 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_69d6aacdbc6c8190af6dc3d5f5d22836 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7fc389d4c81909515a5c8b0099c36 |
completed | April 9, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f12fa07fc081909a42f9c19ad38511 |
completed | April 28, 2026, 10:07 p.m. |
Created at: April 8, 2026, 9:34 p.m.