Triple
T12972968
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nakiska Ski Resort |
E321448
|
entity |
| Predicate | snowmakingCoverage |
P35177
|
FINISHED |
| Object | extensive snowmaking system |
—
|
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: extensive snowmaking system | Statement: [Nakiska Ski Resort, snowmakingCoverage, extensive snowmaking system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: snowmakingCoverage Context triple: [Nakiska Ski Resort, snowmakingCoverage, extensive snowmaking system]
-
A.
snowmaking
chosen
Indicates the artificial production of snow, typically by machines, for use in places like ski slopes or winter recreation areas.
-
B.
snowQuality
Indicates the condition or characteristics of the snow, such as its texture, depth, or suitability for a particular use.
-
C.
hasSnowfall
Indicates that a location or area experiences or contains snowfall.
-
D.
snowCover
Indicates that one entity is covered by or blanketed with snow.
-
E.
featuresSnowEffects
Indicates that something includes or displays visual or environmental effects related to falling or accumulated snow.
- 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_69d80763bd6c819094437da5b20b01d2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97f2a71a0819098bb6cf8a4b2208a |
completed | April 10, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69d97dbdd94c8190ac4bbecca02dc77b |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 8:36 p.m.