Triple
T32040339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wheel Well Butte |
E818203
|
entity |
| Predicate | locatedInRegionInspiredBy |
P14480
|
FINISHED |
| Object | U.S. Route 66 |
E89106
|
NE 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: U.S. Route 66 | Statement: [Wheel Well Butte, locatedInRegionInspiredBy, U.S. Route 66]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locatedInRegionInspiredBy Context triple: [Wheel Well Butte, locatedInRegionInspiredBy, U.S. Route 66]
-
A.
inspiredByPlace
chosen
Indicates that something (such as a work, idea, or creation) originates from or is significantly influenced by a particular location or environment.
-
B.
mentionsRegion
Indicates that one entity explicitly refers to or cites a specific geographic region in its content or context.
-
C.
belongsToFictionalRegion
Indicates that an entity is located within, associated with, or under the jurisdiction of a fictional or imaginary geographic region.
-
D.
primaryRegionDepicted
Indicates that a resource primarily depicts or visually represents a particular geographic or spatial region as its main subject.
-
E.
evokesRegion
Indicates that one entity elicits, suggests, or brings to mind a particular geographic or spatial region.
- F. None of above.
Provenance (4 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_69f348fbc8148190b3c0f95d4772b153 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a0212b3462c8190ba3587852cb10655 |
completed | May 11, 2026, 5:32 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2f011366d4819084ab67602a667ab0 |
completed | June 14, 2026, 7:29 p.m. |
| PD | Predicate disambiguation | batch_6a020f3138a48190ac630e810939e881 |
completed | May 11, 2026, 5:17 p.m. |
Created at: May 1, 2026, 12:19 a.m.