Triple
T1569563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tamil Nadu |
E33506
|
entity |
| Predicate | receivesMonsoonFrom |
P15750
|
FINISHED |
| Object | northeast monsoon |
—
|
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: northeast monsoon | Statement: [Tamil Nadu, receivesMonsoonFrom, northeast monsoon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: receivesMonsoonFrom Context triple: [Tamil Nadu, receivesMonsoonFrom, northeast monsoon]
-
A.
receivesMoistureFrom
chosen
Indicates that one entity obtains or is supplied with moisture (such as water, humidity, or precipitation) from another entity.
-
B.
drySeason
Indicates that the relationship or action occurs during, or is characteristic of, a period with little or no rainfall.
-
C.
hasSeasonalFlooding
Indicates that an area regularly experiences flooding during specific, recurring times of the year.
-
D.
locatedInMountainRainShadowOf
Indicates that one location lies in the rain shadow of a mountain or mountain range, receiving reduced precipitation because the mountains block prevailing moisture-bearing winds.
-
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_69a885f11b048190935025a035302715 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a96083e7308190abbf025fe8e43abb |
completed | March 5, 2026, 10:52 a.m. |
| PD | Predicate disambiguation | batch_69a907ba63c88190b60c14dec8d1e40f |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:27 p.m.