Triple
T2284185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rain City |
E51348
|
entity |
| Predicate | associatedWithPrecipitationType |
P36155
|
FINISHED |
| Object | rain |
—
|
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: rain | Statement: [Rain City, associatedWithPrecipitationType, rain]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithPrecipitationType Context triple: [Rain City, associatedWithPrecipitationType, rain]
-
A.
associatedWithWeather
Indicates a relationship where something is connected or related to weather conditions or phenomena.
-
B.
climatologicalType
Indicates the classification of a climate or weather pattern that characterizes a place, period, or condition.
-
C.
hasExtremeWeatherCharacteristic
Indicates that something possesses a notable or defining feature related to extreme weather conditions.
-
D.
typicalStormType
Indicates the kind of storm that is most commonly or characteristically associated with a given context or location.
-
E.
averageAnnualPrecipitation
Indicates the typical total amount of precipitation an entity receives over the course of a year, averaged across multiple years.
- F. None of above. chosen
Provenance (4 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. |
| PDg | Predicate description generation | batch_69abbe1ecb7081909c2c66da08a48ab7 |
completed | March 7, 2026, 5:56 a.m. |
Created at: March 4, 2026, 7:48 p.m.