Triple
T24501588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Loughrigg Terrace |
E617945
|
entity |
| Predicate | hasNearbyParking |
P36615
|
FINISHED |
| Object | Grasmere village car parks |
—
|
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: Grasmere village car parks | Statement: [Loughrigg Terrace, hasNearbyParking, Grasmere village car parks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyParking Context triple: [Loughrigg Terrace, hasNearbyParking, Grasmere village car parks]
-
A.
hasParkingNearby
chosen
Indicates that a location has one or more parking facilities or spaces available within a close surrounding area.
-
B.
hasNearbyParkEntrance
Indicates that one location is situated close to an entrance of a park.
-
C.
hasParkingFor
Indicates that a place or facility provides designated parking spaces suitable for a specified type of vehicle or user.
-
D.
hasParking
Indicates that a place or facility provides designated parking space(s) available for use.
-
E.
hasBusGarageNearby
Indicates that a bus garage is located in close proximity to the referenced entity.
- 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_69e2d7f682108190a1a7ca5fd485ee8a |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f2a9d912e88190bc39c05a9d7f407e |
completed | April 30, 2026, 1:01 a.m. |
| PD | Predicate disambiguation | batch_69f2a6a4580481908fddc385f5262f95 |
completed | April 30, 2026, 12:47 a.m. |
Created at: April 18, 2026, 2:23 a.m.