Triple
T23584874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheoah River |
E582313
|
entity |
| Predicate | hasActivityRiskLevel |
P150896
|
FINISHED |
| Object | high for paddlers |
—
|
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: high for paddlers | Statement: [Cheoah River, hasActivityRiskLevel, high for paddlers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasActivityRiskLevel Context triple: [Cheoah River, hasActivityRiskLevel, high for paddlers]
-
A.
hasRiskStatus
chosen
Indicates the level or category of risk currently associated with an entity.
-
B.
hasRiskFrom
Indicates that one entity is exposed to or may suffer potential harm, loss, or adverse effects as a result of another entity.
-
C.
hasHazardLevel
Indicates that an entity is associated with a specified degree or category of risk or danger.
-
D.
hasRiskFactorFor
Indicates that one entity contributes to or increases the likelihood of another entity experiencing a particular risk or adverse outcome.
-
E.
hasVehicularActivityLevel
Indicates the degree or intensity of vehicular activity associated with an entity, such as traffic volume or frequency of vehicle use.
- 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_69e248f8d8248190acd5aee77f0d1709 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b02f68288190b348c7558a6a24e1 |
completed | April 29, 2026, 7:15 a.m. |
| PD | Predicate disambiguation | batch_69f118c96a0081908a8ac98ef7e7e60c |
completed | April 28, 2026, 8:30 p.m. |
Created at: April 17, 2026, 6:40 p.m.