Triple
T24285735
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Midori no Hi |
E605663
|
entity |
| Predicate | typicalWeatherAssociation |
P32056
|
FINISHED |
| Object | mild spring weather in Japan |
—
|
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 spring weather in Japan | Statement: [Midori no Hi, typicalWeatherAssociation, mild spring weather in Japan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalWeatherAssociation Context triple: [Midori no Hi, typicalWeatherAssociation, mild spring weather in Japan]
-
A.
typicalWeatherFeature
Indicates a weather condition or pattern that commonly characterizes a place or time period.
-
B.
typicalPrecipitationPattern
Indicates the usual or characteristic pattern of precipitation associated with a place, time period, or climate condition.
-
C.
typicalWeatherNorthernHemisphere
Indicates the characteristic or commonly occurring weather conditions found in the Northern Hemisphere.
-
D.
associatedWithWeather
chosen
Indicates a relationship where something is connected or related to weather conditions or phenomena.
-
E.
typicalWeatherSouthernHemisphere
Indicates that the described weather conditions are characteristic or commonly experienced in the Southern Hemisphere.
- 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_69e295480d0c8190846fc3c2e2da1d4c |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f28f5605f081908367bd08ab1ec9ab |
completed | April 29, 2026, 11:08 p.m. |
| PD | Predicate disambiguation | batch_69f1c457a2908190993824395b3c365d |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 18, 2026, 12:08 a.m.