Triple
T14997958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Snowshoe |
E374008
|
entity |
| Predicate | hasSkiAreaVerticalDrop |
P116280
|
FINISHED |
| Object | approximately 1500 feet |
—
|
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: approximately 1500 feet | Statement: [Snowshoe, hasSkiAreaVerticalDrop, approximately 1500 feet]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSkiAreaVerticalDrop Context triple: [Snowshoe, hasSkiAreaVerticalDrop, approximately 1500 feet]
-
A.
hasSkiLifts
Indicates that one location or facility is equipped with ski lifts that provide transportation for skiers or visitors.
-
B.
hasSkiRunsLength_km
Indicates the total length, in kilometers, of the ski runs associated with an entity.
-
C.
hasSkiAreaSide
Indicates that something is located on or associated with a particular side or slope of a ski area.
-
D.
hasSkiResortType
Indicates that an entity is associated with, or classified by, a specific type or category of ski resort.
-
E.
hasSkiableArea
Indicates that an entity possesses an area of terrain that can be used for skiing.
- F. None of above. chosen
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_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. |
| PDg | Predicate description generation | batch_69deb1a88d588190996afa8e5b32b552 |
completed | April 14, 2026, 9:29 p.m. |
Created at: April 10, 2026, 2:54 a.m.