Triple
T7949009
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fremont Indian State Park and Museum |
E184565
|
entity |
| Predicate | nearbyRegionInfluence |
P19397
|
FINISHED |
| Object | Sevier County tourism |
—
|
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: Sevier County tourism | Statement: [Fremont Indian State Park and Museum, nearbyRegionInfluence, Sevier County tourism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyRegionInfluence Context triple: [Fremont Indian State Park and Museum, nearbyRegionInfluence, Sevier County tourism]
-
A.
featuresRegionalProximity
Indicates that one entity is located near or in close geographic proximity to a particular region or another entity.
-
B.
nearbyTo
Indicates that one entity is located close in distance or position to another entity.
-
C.
influencesRegion
chosen
Indicates that one entity has an effect on, shapes, or alters the conditions, characteristics, or behavior of a specified region.
-
D.
nearbyLocation
Indicates that one location is situated close to another location in physical space.
-
E.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
- 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_69ca8291c2008190b1b8832c87814bcf |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3b2bf6f48190ac7491c41045cab2 |
completed | March 31, 2026, 3:10 a.m. |
| PD | Predicate disambiguation | batch_69cae9361bc48190886b7681e563d46b |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:10 p.m.