Triple
T30854591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Köppen Cfa |
E785883
|
entity |
| Predicate | hasWinterPrecipitation |
P107020
|
FINISHED |
| Object | significant winter precipitation |
—
|
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: significant winter precipitation | Statement: [Köppen Cfa, hasWinterPrecipitation, significant winter precipitation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWinterPrecipitation Context triple: [Köppen Cfa, hasWinterPrecipitation, significant winter precipitation]
-
A.
hasWinterPhenomenon
Indicates that an entity experiences or is characterized by a particular phenomenon occurring during the winter season.
-
B.
hasSnowfall
Indicates that a location or area experiences or contains snowfall.
-
C.
hasWinterSeason
Indicates that an entity experiences or includes a distinct winter season within its annual climate or temporal cycle.
-
D.
hasSnowAndIce
Indicates that the subject is covered with or contains both snow and ice.
-
E.
hasPrecipitationCriterion
chosen
Indicates that something is subject to, defined by, or must satisfy a specified condition related to precipitation (such as amount, type, or occurrence of rainfall, snow, etc.).
- 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_69f224b91c14819084e764832fe67a57 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6d0d46aec819091edf97324d793ac |
completed | May 3, 2026, 4:36 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe2183481908ae4e85a59c66f69 |
completed | May 3, 2026, 4:32 a.m. |
Created at: April 29, 2026, 8:46 p.m.