Triple
T1902202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bluewater |
E37713
|
entity |
| Predicate | hasCarParkingSpaces |
P21999
|
FINISHED |
| Object | over 13000 |
—
|
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: over 13000 | Statement: [Bluewater, hasCarParkingSpaces, over 13000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCarParkingSpaces Context triple: [Bluewater, hasCarParkingSpaces, over 13000]
-
A.
numberOfParkingSpaces
chosen
Indicates the total count of parking spaces associated with a particular entity or location.
-
B.
hasParking
Indicates that a place or facility provides designated parking space(s) available for use.
-
C.
parkingType
Indicates the specific kind or category of parking arrangement associated with an entity (e.g., street, garage, lot, reserved).
-
D.
hasParkArea
Indicates that an entity includes or is associated with a designated park or recreational area within its boundaries.
-
E.
parkingStructure
Indicates that one entity is a parking facility or structure associated with another entity (such as a building, location, or organization).
- 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_69a8861be7148190a680937ec451a304 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb34d94fc8190a5bf1e582c77c725 |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abafe9f8b0819086d8f6288511c66d |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:35 p.m.