Triple
T1207184
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Derbion |
E25915
|
entity |
| Predicate | hasParkingSpaces |
P21999
|
FINISHED |
| Object | 3500 |
—
|
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: 3500 | Statement: [Derbion, hasParkingSpaces, 3500]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasParkingSpaces Context triple: [Derbion, hasParkingSpaces, 3500]
-
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.
parkSystem
Indicates a relationship where an entity is part of, managed by, or associated with an organized system of parks or protected recreational areas.
-
E.
hasBusBays
Indicates that a location or facility is equipped with one or more designated bus bays for buses to stop, load, or unload passengers.
- 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_69a4942b30f08190a91c60573e16b5ef |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bdc483b481908e5bfef5d2f4fc5a |
completed | March 1, 2026, 10:29 p.m. |
| PD | Predicate disambiguation | batch_69a4bb6078088190ba0221ae3368416c |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:46 p.m.