Triple
T14997963
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Snowshoe |
E374008
|
entity |
| Predicate | hasSkiAreaSnowmakingCoverage |
P35177
|
FINISHED |
| Object | high percentage of terrain |
—
|
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 percentage of terrain | Statement: [Snowshoe, hasSkiAreaSnowmakingCoverage, high percentage of terrain]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSkiAreaSnowmakingCoverage Context triple: [Snowshoe, hasSkiAreaSnowmakingCoverage, high percentage of terrain]
-
A.
hasSnowPark
Indicates that a location or facility includes or is equipped with a designated snow park area.
-
B.
hasSkiCenter
Indicates that a location or entity possesses or hosts a ski center as one of its facilities or features.
-
C.
hasSkiPatrol
Indicates that an entity is served or overseen by a ski patrol responsible for safety and emergency response on the slopes.
-
D.
snowmaking
chosen
Indicates the artificial production of snow, typically by machines, for use in places like ski slopes or winter recreation areas.
-
E.
hasSkiAreaSide
Indicates that something is located on or associated with a particular side or slope of a ski area.
- 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_69d85ccc84388190aa151e5173370c8d |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded71a5618819083ae96a79735ef98 |
completed | April 15, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69de9a6169b48190a679609febd2d0e3 |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:54 a.m.