Triple
T10013174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Whernside (part) |
E199423
|
entity |
| Predicate | hasPrimaryAccessUse |
P85170
|
FINISHED |
| Object | public footpaths and bridleways |
—
|
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: public footpaths and bridleways | Statement: [Whernside (part), hasPrimaryAccessUse, public footpaths and bridleways]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPrimaryAccessUse Context triple: [Whernside (part), hasPrimaryAccessUse, public footpaths and bridleways]
-
A.
hasPrimaryAccessType
chosen
Indicates that an entity is associated with its main or default mode or category of access.
-
B.
hasPrimary
Indicates that one entity is designated as the main or most important instance (the primary) in relation to another entity.
-
C.
hasHumanAccess
Indicates that a human is able to access, use, or interact with the referenced entity or resource.
-
D.
hasSecondaryUsage
Indicates that an entity is associated with an additional, non-primary function or purpose beyond its main intended use.
-
E.
hasPrimaryFeature
Indicates that an entity possesses a main or most characteristic feature that defines or distinguishes it.
- 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_69ca8315a1a08190ab310f25620f362b |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cdcd3e35508190920468be167cb708 |
completed | April 2, 2026, 1:58 a.m. |
| PD | Predicate disambiguation | batch_69cd1da2cf9081908a6c0eb5247d0bc2 |
completed | April 1, 2026, 1:29 p.m. |
Created at: March 30, 2026, 8:52 p.m.