Triple
T2284183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rain City |
E51348
|
entity |
| Predicate | associatedWithWeatherPattern |
P32056
|
FINISHED |
| Object | Pacific maritime 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: Pacific maritime climate | Statement: [Rain City, associatedWithWeatherPattern, Pacific maritime climate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithWeatherPattern Context triple: [Rain City, associatedWithWeatherPattern, Pacific maritime climate]
-
A.
associatedWithWeather
chosen
Indicates a relationship where something is connected or related to weather conditions or phenomena.
-
B.
hasExtremeWeatherCharacteristic
Indicates that something possesses a notable or defining feature related to extreme weather conditions.
-
C.
hasWeather
Indicates that a location or environment is experiencing or characterized by a particular type of weather condition.
-
D.
oftenAccompaniedBy
Indicates that one entity is frequently found together with, occurs alongside, or is commonly associated in presence or use with another entity.
-
E.
associatedWithSee
Indicates a relationship where one entity is contextually or functionally linked to another through the act or concept of seeing or visual observation.
- 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_69a88b08e4308190bdac9aebcca1c91a |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abc2445f388190af643878145f8249 |
completed | March 7, 2026, 6:14 a.m. |
| PD | Predicate disambiguation | batch_69abbdbb9e4c819085fc588626ec7c09 |
completed | March 7, 2026, 5:55 a.m. |
Created at: March 4, 2026, 7:48 p.m.