Triple
T9506007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Bachelor ski area |
E229269
|
entity |
| Predicate | hasSnowfallAverage |
P10513
|
FINISHED |
| Object | over 400 inches per year |
—
|
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: over 400 inches per year | Statement: [Mount Bachelor ski area, hasSnowfallAverage, over 400 inches per year]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSnowfallAverage Context triple: [Mount Bachelor ski area, hasSnowfallAverage, over 400 inches per year]
-
A.
averageAnnualSnowfall
chosen
Indicates the typical amount of snow that falls in a given location over the course of a year, averaged across multiple years.
-
B.
hasSnowfall
Indicates that a location or area experiences or contains snowfall.
-
C.
hasSnowfallUnit
Indicates the unit of measurement used to express the amount or depth of snowfall in a given context.
-
D.
hasSnowOccasionally
Indicates that the subject experiences snowfall at irregular or infrequent intervals rather than regularly or never.
-
E.
hasSnowIn
Indicates that snow is present or occurs within a specified location or region.
- 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_69ca847611c48190a28c028644198c75 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9852b7e48190a8f69cbde10d2858 |
completed | April 1, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69cca567ca448190bf4bcce8ce7dd54f |
completed | April 1, 2026, 4:56 a.m. |
Created at: March 30, 2026, 7:57 p.m.