Triple
T8318530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dunkin' |
E194768
|
entity |
| Predicate | formerName |
P65
|
FINISHED |
| Object | Dunkin' Donuts |
E194768
|
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: Dunkin' Donuts | Statement: [Dunkin', formerName, Dunkin' Donuts]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dunkin' Donuts Context triple: [Dunkin', formerName, Dunkin' Donuts]
-
A.
Dunkin'
chosen
Dunkin' is a major American coffee and baked goods chain best known for its donuts and wide variety of coffee beverages.
-
B.
Krispy Kreme
Krispy Kreme is an American doughnut company and coffeehouse chain best known for its Original Glazed doughnuts and signature “Hot Now” sign.
-
C.
Panera Bread
Panera Bread is a popular American fast-casual restaurant and bakery chain known for its soups, salads, sandwiches, and freshly baked bread.
-
D.
Starbucks
Starbucks is a global coffeehouse chain and coffee roastery brand known for its specialty coffee drinks and widespread presence in cities around the world.
-
E.
McDonald’s
McDonald’s is a global fast-food restaurant chain best known for its hamburgers, fries, and iconic Golden Arches 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_69ca82e6e2648190a31eaf6f4f757b2a |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7f648e10819081ad1fed870b2b86 |
completed | March 31, 2026, 8:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd9596891c81909296050d0a8117ca |
completed | April 1, 2026, 10 p.m. |
Created at: March 30, 2026, 5:55 p.m.