Triple
T7428912
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tug Hill Plateau |
E171435
|
entity |
| Predicate | snowfallSource |
P69360
|
FINISHED |
| Object | lake-effect snow from Lake Ontario |
—
|
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: lake-effect snow from Lake Ontario | Statement: [Tug Hill Plateau, snowfallSource, lake-effect snow from Lake Ontario]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: snowfallSource Context triple: [Tug Hill Plateau, snowfallSource, lake-effect snow from Lake Ontario]
-
A.
primarySnowSource
chosen
Indicates that one entity serves as the main origin or contributor of snow for another entity or location.
-
B.
hasSnowfall
Indicates that a location or area experiences or contains snowfall.
-
C.
snowCover
Indicates that one entity is covered by or blanketed with snow.
-
D.
snowfallRecord
Indicates that a specific amount of snow has been measured or documented for a particular place and time.
-
E.
snowQuality
Indicates the condition or characteristics of the snow, such as its texture, depth, or suitability for a particular use.
- 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_69c68a63491881909281f73d4d5643bf |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f3082f188190af5673d18ac7e87e |
completed | March 27, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69c6f03648d08190b862d07fef71210c |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:12 p.m.