Triple
T19180814
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toyokawa |
E469565
|
entity |
| Predicate | climateClassification |
P193
|
FINISHED |
| Object | Köppen Cfa |
—
|
NE NERFINISHED |
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: Köppen Cfa | Statement: [Toyokawa, climateClassification, Köppen Cfa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Köppen Cfa Context triple: [Toyokawa, climateClassification, Köppen Cfa]
-
A.
Köppen Cfa
chosen
Köppen Cfa is a humid subtropical climate type characterized by hot, humid summers and mild winters with no distinct dry season.
-
B.
Köppen Cwa
Köppen Cwa is a humid subtropical climate type characterized by hot, wet summers and mild, dry winters, typically influenced by monsoonal patterns.
-
C.
Köppen Cfb
Köppen Cfb is a temperate oceanic climate type characterized by mild temperatures year-round, no dry season, and warm (but not hot) summers.
-
D.
Köppen Aw
Köppen Aw is a tropical savanna climate type characterized by consistently warm temperatures and a pronounced dry season.
-
E.
Köppen BWh
Köppen BWh is the hot desert climate subtype characterized by extremely low annual precipitation, very high temperatures, and abundant sunshine, typical of the world’s driest desert regions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dd09d5a081909ae43c286651ae5a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f61be86c81908710341262e911cd |
completed | April 20, 2026, 9:47 a.m. |
Created at: April 10, 2026, 12:07 p.m.