Triple
T33290610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Europa Passage |
E852308
|
entity |
| Predicate | hasRestaurantAndCafeCount |
—
|
GENERATED |
| Object | about 20 |
—
|
UNRECOGNIZED GENERATED |
How this triple was built (1 step)
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.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRestaurantAndCafeCount Context triple: [Europa Passage, hasRestaurantAndCafeCount, about 20]
-
A.
numberOfRestaurantsAndCafes
chosen
Indicates the total count of restaurants and cafes associated with a given entity or area.
-
B.
hasRestaurantsAndCafes
Indicates that the subject location contains or provides access to restaurants and cafés.
-
C.
hasNumberOfRestaurantsAndBars
Indicates the total count of restaurants and bars associated with a given entity.
-
D.
numberOfRestaurants
Indicates the quantitative count of restaurants associated with a given entity or context.
-
E.
numberOfRestaurantsAndRetail
Indicates the total count of entities that are either restaurants or retail establishments associated with a given subject.
- F. None of above.
Provenance (1 batch)
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_69f349660ff48190a4568803d0b89941 |
completed | April 30, 2026, 12:21 p.m. |
Created at: May 1, 2026, 1:32 a.m.