Triple
T6890149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kota Hujan |
E159023
|
entity |
| Predicate | associatedWeatherPhenomenon |
P32056
|
FINISHED |
| Object | orographic rainfall |
—
|
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: orographic rainfall | Statement: [Kota Hujan, associatedWeatherPhenomenon, orographic rainfall]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWeatherPhenomenon Context triple: [Kota Hujan, associatedWeatherPhenomenon, orographic rainfall]
-
A.
associatedWithPrecipitationType
Indicates that there is a relationship between an entity and a specific type or category of precipitation (such as rain, snow, or hail).
-
B.
associatedWithWeather
chosen
Indicates a relationship where something is connected or related to weather conditions or phenomena.
-
C.
hasNaturalPhenomenon
Indicates that a location, region, or environment possesses or is characterized by a particular natural phenomenon (such as a weather event, geological feature, or celestial occurrence).
-
D.
hasExtremeWeatherCharacteristic
Indicates that something possesses a notable or defining feature related to extreme weather conditions.
-
E.
containsMajorClimatePhenomenon
Indicates that the subject region or area includes or experiences a significant, large-scale climate-related event or pattern.
- 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_69c6883568c8819081db6407e892cccc |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d92d45f08190a730b3842c95b521 |
completed | March 27, 2026, 7:23 p.m. |
| PD | Predicate disambiguation | batch_69c6d7b53e9881909ec298daa9f1913b |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:23 p.m.