Triple
T33290610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Europa Passage |
E852308
|
entity |
| Predicate | hasRestaurantAndCafeCount |
P87876
|
FINISHED |
| Object | about 20 |
—
|
LITERAL 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: about 20 | Statement: [Europa Passage, hasRestaurantAndCafeCount, about 20]
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 (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_69f349660ff48190a4568803d0b89941 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a036c1282748190a820562bcd9aa5d9 |
completed | May 12, 2026, 6:06 p.m. |
| PD | Predicate disambiguation | batch_6a036bb5ca0c8190bd50abc197960b41 |
completed | May 12, 2026, 6:04 p.m. |
Created at: May 1, 2026, 1:32 a.m.