Triple
T17077553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caffè Nero |
E414388
|
entity |
| Predicate | competesWith |
P1375
|
FINISHED |
| Object | Costa Coffee |
E85138
|
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: Costa Coffee | Statement: [Caffè Nero, competesWith, Costa Coffee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Costa Coffee Context triple: [Caffè Nero, competesWith, Costa Coffee]
-
A.
Costa Coffee
chosen
Costa Coffee is a major British coffeehouse chain known for its espresso-based drinks and café-style food, operating thousands of outlets worldwide.
-
B.
Caffè Nero
Caffè Nero is a European-style coffeehouse chain known for its Italian-inspired espresso drinks and relaxed café atmosphere.
-
C.
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.
-
D.
Carousel Coffee
Carousel Coffee is a casual coffee shop located on Disney’s BoardWalk, serving specialty beverages and light refreshments to guests in the entertainment district.
-
E.
McCafé
McCafé is McDonald's in-house coffeehouse-style chain offering specialty coffee drinks, pastries, and café-style food items.
- 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_69d886cef44c8190ba56c44b4e863e64 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbc625c48190b679a521180e10ad |
completed | April 18, 2026, 7:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0180c3be308190ab9972e79287218b |
completed | May 11, 2026, 7:09 a.m. |
Created at: April 10, 2026, 5:34 a.m.