Triple
T21616905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kingsbury Grade |
E533466
|
entity |
| Predicate | hasScenicClassification |
P129433
|
FINISHED |
| Object | scenic mountain road |
—
|
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: scenic mountain road | Statement: [Kingsbury Grade, hasScenicClassification, scenic mountain road]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasScenicClassification Context triple: [Kingsbury Grade, hasScenicClassification, scenic mountain road]
-
A.
hasScenicValue
Indicates that something possesses notable aesthetic or visual appeal, often due to its natural beauty or pleasing surroundings.
-
B.
isScenicArea
Indicates that a location is recognized as a scenic area, typically valued for its natural beauty or visually appealing surroundings.
-
C.
hasScenicResource
Indicates that an entity possesses or is associated with a natural or visual feature valued for its aesthetic or scenic qualities.
-
D.
hasScenicRouteType
chosen
Indicates that a route is associated with a specific type or category of scenic quality or scenic designation.
-
E.
hasScenicSections
Indicates that a route, path, or area contains segments that are visually attractive or offer notable scenic views.
- 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_69e0c46411108190bba0d4176dffc9f3 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef3bab865081908bc7d7b1824415ac |
completed | April 27, 2026, 10:34 a.m. |
| PD | Predicate disambiguation | batch_69e69665fe8c8190af7e38785db188b2 |
completed | April 20, 2026, 9:11 p.m. |
Created at: April 16, 2026, 6:34 p.m.