Triple
T9953648
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krusty Burger |
E195391
|
entity |
| Predicate | parodies |
P10352
|
FINISHED |
| Object | Burger King |
E723224
|
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: Burger King | Statement: [Krusty Burger, parodies, Burger King]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Burger King Context triple: [Krusty Burger, parodies, Burger King]
-
A.
Burger King
chosen
Burger King is a global fast-food restaurant chain known for flame-grilled burgers such as the Whopper.
-
B.
McDonald’s
McDonald’s is a global fast-food restaurant chain best known for its hamburgers, fries, and iconic Golden Arches branding.
-
C.
Wendy's
Wendy's is a major American fast-food restaurant chain best known for its square hamburgers, Frosty desserts, and distinctive redheaded girl logo.
-
D.
Carl's Jr.
Carl's Jr. is an American fast-food restaurant chain known for its charbroiled burgers and often provocative advertising campaigns.
-
E.
Hardee
Hardee is a surname most notably associated with William J. Hardee, a 19th-century United States Army officer and Confederate general during the American Civil War.
- 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_69ca82eaaa008190a54fa1a9f954b9ad |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb693918081908e9f96ef302235ad |
completed | April 2, 2026, 12:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23d6743c481908a2040eb9d260b4a |
completed | April 5, 2026, 10:45 a.m. |
Created at: March 30, 2026, 8:46 p.m.