Triple
T11416062
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daws Butler |
E270494
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Yogi Bear |
E269724
|
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: Yogi Bear | Statement: [Daws Butler, notableWork, Yogi Bear]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yogi Bear Context triple: [Daws Butler, notableWork, Yogi Bear]
-
A.
Yogi Bear
chosen
Yogi Bear is a classic animated cartoon character known as a mischievous, picnic-basket-stealing bear from Jellystone Park.
-
B.
Fozzie Bear
Fozzie Bear is a lovable, joke-telling bear from The Muppets known for his corny humor and signature catchphrase, "Wocka wocka!"
-
C.
Boomer the Bear
Boomer the Bear is the costumed bear mascot who represents Missouri State University at athletic events and campus activities.
-
D.
Joe Bear
Joe Bear is the costumed bear mascot that represents Lenoir–Rhyne University at its athletic events and campus activities.
-
E.
Brisky the Bear
Brisky the Bear is the costumed bear mascot of Japan’s Hokkaido Nippon-Ham Fighters professional baseball team, known for entertaining fans at games and team events.
- 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_69d6aaddeaa8819088b30ef7b50598c9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d801ae47d0819098123505309c4a68 |
completed | April 9, 2026, 7:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ee864bf89081909fa336393e59f073 |
completed | April 26, 2026, 9:40 p.m. |
Created at: April 8, 2026, 9:34 p.m.