Triple
T32859356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | town of Osceola |
E840468
|
entity |
| Predicate | snowfallType |
P45704
|
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: [town of Osceola, snowfallType, lake-effect snow from Lake Ontario]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: snowfallType Context triple: [town of Osceola, snowfallType, lake-effect snow from Lake Ontario]
-
A.
hasSnowType
chosen
Indicates that something possesses or is characterized by a particular type or category of snow.
-
B.
hasSnowfall
Indicates that a location or area experiences or contains snowfall.
-
C.
snowAccumulation
Indicates that snow has collected or built up on a surface or in a location over time.
-
D.
snowQuality
Indicates the condition or characteristics of the snow, such as its texture, depth, or suitability for a particular use.
-
E.
hasSnowfallUnit
Indicates the unit of measurement used to express the amount or depth of snowfall in a given context.
- 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_69f34942465c819099b3fb47f9044f58 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6ceb3673481908d3d2cb2921ff8dd |
completed | May 3, 2026, 4:27 a.m. |
| PD | Predicate disambiguation | batch_69f6cc177b288190904f3f23cb856d8b |
completed | May 3, 2026, 4:16 a.m. |
Created at: May 1, 2026, 1:17 a.m.