Triple
T657633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sugar Bowl Resort |
E11682
|
entity |
| Predicate | hasLiftCount |
P7664
|
FINISHED |
| Object | approximately 12 lifts |
—
|
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 12 lifts | Statement: [Sugar Bowl Resort, hasLiftCount, approximately 12 lifts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLiftCount Context triple: [Sugar Bowl Resort, hasLiftCount, approximately 12 lifts]
-
A.
hasLiftType
Indicates the specific type or category of lift associated with an entity.
-
B.
verticalLiftSpanLength
Indicates the length of the span that moves vertically in a lifting structure or mechanism.
-
C.
hasHeadCount
Indicates that an entity is associated with a specific number of individuals, typically representing the size or count of people (or similar units) related to it.
-
D.
hasNumberOfDrops
Indicates the quantity or count of drops associated with an entity or event.
-
E.
hasTotalNumber
chosen
Indicates that an entity is associated with a specific overall count or sum of items, elements, or units.
- 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_69a4932862a0819098be659c814e4981 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49fa55e048190bd9913c6c31772d0 |
completed | March 1, 2026, 8:20 p.m. |
| PD | Predicate disambiguation | batch_69a49d121cec81909986c91291bb4ca8 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:36 p.m.