Triple
T17120032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Herman Cain |
E415439
|
entity |
| Predicate | employer |
P7
|
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: [Herman Cain, employer, Burger King]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Burger King Context triple: [Herman Cain, employer, 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.
Burger World
Burger World is a fictional fast-food restaurant featured in the animated series "Beavis and Butt-Head," where the main characters work.
-
E.
Whataburger
Whataburger is a regional American fast-food chain founded in Texas, best known for its large made-to-order hamburgers and distinctive orange-and-white branding.
- 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_69d886d090cc8190a39cb94992586905 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3e8092b548190b45c1695be47edc2 |
completed | April 18, 2026, 8:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a013a0e11108190bfcf858142aa9ff3 |
completed | May 11, 2026, 2:08 a.m. |
Created at: April 10, 2026, 5:36 a.m.