Triple
T12114329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Juanita, Washington |
E288515
|
entity |
| Predicate | typicalPrecipitationPattern |
P103365
|
FINISHED |
| Object | wet winters and dry summers |
—
|
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: wet winters and dry summers | Statement: [Juanita, Washington, typicalPrecipitationPattern, wet winters and dry summers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalPrecipitationPattern Context triple: [Juanita, Washington, typicalPrecipitationPattern, wet winters and dry summers]
-
A.
associatedWithPrecipitationType
Indicates that there is a relationship between an entity and a specific type or category of precipitation (such as rain, snow, or hail).
-
B.
averageAnnualPrecipitation
Indicates the typical total amount of precipitation an entity receives over the course of a year, averaged across multiple years.
-
C.
typicalStormType
Indicates the kind of storm that is most commonly or characteristically associated with a given context or location.
-
D.
typicalWeatherNorthernHemisphere
Indicates the characteristic or commonly occurring weather conditions found in the Northern Hemisphere.
-
E.
typicalTemperature
Indicates the usual or characteristic temperature associated with an entity under normal conditions.
- F. None of above. chosen
Provenance (4 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_69d6ab4a5c448190a110d1273314b21a |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9164ada5081908676bd9e5947268a |
completed | April 10, 2026, 3:24 p.m. |
| PD | Predicate disambiguation | batch_69d9150497408190921334d21503375a |
completed | April 10, 2026, 3:19 p.m. |
| PDg | Predicate description generation | batch_69d916481a008190ae66677b9e6dd961 |
completed | April 10, 2026, 3:24 p.m. |
Created at: April 8, 2026, 9:49 p.m.