Triple
T15454476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nivôse |
E371732
|
entity |
| Predicate | hasCharacteristicWeatherAssociation |
P32056
|
FINISHED |
| Object | snowy conditions |
—
|
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: snowy conditions | Statement: [Nivôse, hasCharacteristicWeatherAssociation, snowy conditions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCharacteristicWeatherAssociation Context triple: [Nivôse, hasCharacteristicWeatherAssociation, snowy conditions]
-
A.
associatedWithWeather
chosen
Indicates a relationship where something is connected or related to weather conditions or phenomena.
-
B.
hasExtremeWeatherCharacteristic
Indicates that something possesses a notable or defining feature related to extreme weather conditions.
-
C.
hasWeather
Indicates that a location or environment is experiencing or characterized by a particular type of weather condition.
-
D.
associatedWithPrecipitationType
Indicates that there is a relationship between an entity and a specific type or category of precipitation (such as rain, snow, or hail).
-
E.
hasClimate
Indicates that an entity possesses or is characterized by a particular type of climate or climatic conditions.
- 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_69d85cc8bd308190886949510b42e764 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f131b1481909ff099c3b844ee07 |
completed | April 16, 2026, 1:44 a.m. |
| PD | Predicate disambiguation | batch_69ded28276f481908c2038bb301e57cf |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:31 a.m.