Triple
T10890001
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Highcross Leicester |
E257147
|
entity |
| Predicate | hasNumberOfCarParkingSpaces |
P21999
|
FINISHED |
| Object | over 3000 |
—
|
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 3000 | Statement: [Highcross Leicester, hasNumberOfCarParkingSpaces, over 3000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfCarParkingSpaces Context triple: [Highcross Leicester, hasNumberOfCarParkingSpaces, over 3000]
-
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.
hasUndergroundParking
Indicates that a place or building provides parking facilities located below ground level.
-
D.
hasParkingFor
Indicates that a place or facility provides designated parking spaces suitable for a specified type of vehicle or user.
-
E.
parkingRequirement
Indicates the specified conditions or obligations related to providing or using parking associated with an entity or activity.
- 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_69d6aa848804819081b2713ca0bedf06 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d752041e2c8190b513dc9dc5857fcc |
completed | April 9, 2026, 7:15 a.m. |
| PD | Predicate disambiguation | batch_69d70d3943c881908895397eccc3e415 |
completed | April 9, 2026, 2:21 a.m. |
Created at: April 8, 2026, 9:21 p.m.