Triple
T25732329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arizona State Route 89 |
E645274
|
entity |
| Predicate | hasScenicSegmentNear |
P35458
|
FINISHED |
| Object | Prescott, Arizona |
—
|
NE NERFINISHED |
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: Prescott, Arizona | Statement: [Arizona State Route 89, hasScenicSegmentNear, Prescott, Arizona]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasScenicSegmentNear Context triple: [Arizona State Route 89, hasScenicSegmentNear, Prescott, Arizona]
-
A.
hasScenicPassNearby
Indicates that a location is situated close to a notable scenic pass, such as a mountain or landscape viewpoint route.
-
B.
hasScenicSections
chosen
Indicates that a route, path, or area contains segments that are visually attractive or offer notable scenic views.
-
C.
hasScenicRouteType
Indicates that a route is associated with a specific type or category of scenic quality or scenic designation.
-
D.
hasScenicAccessTo
Indicates that one place or object provides a visually appealing or notable view of another place or object.
-
E.
hasScenicResource
Indicates that an entity possesses or is associated with a natural or visual feature valued for its aesthetic or scenic qualities.
- 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_69e77e85254081908d79ee4e8715f283 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f6562fd3488190be1acd8c526a28d2 |
completed | May 2, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69f651a731508190bb0c8c2462eba224 |
completed | May 2, 2026, 7:33 p.m. |
Created at: April 21, 2026, 11:17 p.m.