Triple
T8411903
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chimacum, Washington |
E198642
|
entity |
| Predicate | hasAverageClimateCharacteristic |
P29133
|
FINISHED |
| Object | mild, wet winters |
—
|
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: mild, wet winters | Statement: [Chimacum, Washington, hasAverageClimateCharacteristic, mild, wet winters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAverageClimateCharacteristic Context triple: [Chimacum, Washington, hasAverageClimateCharacteristic, mild, wet winters]
-
A.
hasClimate
Indicates that an entity possesses or is characterized by a particular type of climate or climatic conditions.
-
B.
hasClimateContext
Indicates that something is associated with, influenced by, or relevant to climate-related conditions, factors, or considerations.
-
C.
hasExtremeWeatherCharacteristic
Indicates that something possesses a notable or defining feature related to extreme weather conditions.
-
D.
hasMediterraneanClimate
Indicates that a place experiences a Mediterranean climate, typically characterized by mild, wet winters and hot, dry summers.
-
E.
climatologicalType
chosen
Indicates the classification of a climate or weather pattern that characterizes a place, period, or condition.
- 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_69ca831201b481909e137936ef99ff11 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb83e0341c819080506e696131671e |
completed | March 31, 2026, 8:20 a.m. |
| PD | Predicate disambiguation | batch_69cb70d473dc8190af8ea81ee5aa970d |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 6:05 p.m.