Triple
T27033330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Goosenecks State Park |
E680982
|
entity |
| Predicate | hasScenicBywayAccess |
P13952
|
FINISHED |
| Object | near U.S. Route 163 |
—
|
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: near U.S. Route 163 | Statement: [Goosenecks State Park, hasScenicBywayAccess, near U.S. Route 163]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasScenicBywayAccess Context triple: [Goosenecks State Park, hasScenicBywayAccess, near U.S. Route 163]
-
A.
hasScenicByway
chosen
Indicates that one place, route, or area is connected to or includes a designated scenic byway.
-
B.
isScenicByway
Indicates that a roadway is officially designated as a scenic byway, recognized for its notable visual, cultural, or natural appeal along its route.
-
C.
hasScenicAccessTo
Indicates that one place or object provides a visually appealing or notable view of another place or object.
-
D.
hasScenicRoutes
Indicates that one location, path, or area offers routes that are visually attractive or notable for their natural or aesthetic scenery.
-
E.
hasScenicDrive
Indicates that one entity offers or features a visually appealing or picturesque driving route associated with 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_69eeeb5566f08190813daf896fa3da04 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f6a8df16a88190a23820e64a3b1f92 |
completed | May 3, 2026, 1:46 a.m. |
| PD | Predicate disambiguation | batch_69f6a751d5e48190a77dcecbe7ef9f0b |
completed | May 3, 2026, 1:39 a.m. |
Created at: April 27, 2026, 7:15 a.m.