Triple
T6174714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Temperate Zone |
E137789
|
entity |
| Predicate | hasTypicalClimateType |
P29133
|
FINISHED |
| Object | humid subtropical climate |
—
|
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: humid subtropical climate | Statement: [Temperate Zone, hasTypicalClimateType, humid subtropical climate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalClimateType Context triple: [Temperate Zone, hasTypicalClimateType, humid subtropical climate]
-
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.
climatologicalType
chosen
Indicates the classification of a climate or weather pattern that characterizes a place, period, or condition.
-
D.
hasMediterraneanClimate
Indicates that a place experiences a Mediterranean climate, typically characterized by mild, wet winters and hot, dry summers.
-
E.
hasExtremeWeatherCharacteristic
Indicates that something possesses a notable or defining feature related to extreme weather 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_69c008a80f748190ba3d07ffc81acb29 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05dc55b5c819084482b735771c9a8 |
completed | March 22, 2026, 9:23 p.m. |
| PD | Predicate disambiguation | batch_69c055f7f12881908e21c04e9b752ba4 |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:18 p.m.