Triple
T17899113
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Village of Cattaraugus, New York |
E447514
|
entity |
| Predicate | typicalWinterWeather |
P10789
|
FINISHED |
| Object | cold and snowy |
—
|
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: cold and snowy | Statement: [Village of Cattaraugus, New York, typicalWinterWeather, cold and snowy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalWinterWeather Context triple: [Village of Cattaraugus, New York, typicalWinterWeather, cold and snowy]
-
A.
winterCharacteristic
chosen
Indicates a characteristic, feature, or quality that is specifically associated with or typical of winter.
-
B.
winterStatus
Indicates the condition, phase, or circumstances associated with the winter season for a given entity or context.
-
C.
wintersIn
Indicates that an entity spends the winter season in a particular place or region.
-
D.
typicalPrecipitationPattern
Indicates the usual or characteristic pattern of precipitation associated with a place, time period, or climate condition.
-
E.
minimumWinterTemperature
Indicates the lowest temperature typically experienced during the winter season for the subject entity.
- 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_69d8b9f59bd48190a6fc925a855b8bac |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49d81e00881908a46305af66fdf1a |
completed | April 19, 2026, 9:16 a.m. |
| PD | Predicate disambiguation | batch_69e3d8e9b77c8190bbfb508f28dfacfa |
completed | April 18, 2026, 7:18 p.m. |
Created at: April 10, 2026, 10:19 a.m.