Triple
T8495757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kilinochchi District |
E201093
|
entity |
| Predicate | monsoonInfluence |
P1886
|
FINISHED |
| Object | North-East 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: North-East monsoon | Statement: [Kilinochchi District, monsoonInfluence, North-East monsoon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: monsoonInfluence Context triple: [Kilinochchi District, monsoonInfluence, North-East monsoon]
-
A.
hasClimateInfluence
Indicates that one entity affects or contributes to the climate characteristics or climate-related conditions of another entity.
-
B.
rainfallImpact
Indicates how rainfall influences or alters the condition, behavior, or outcome of a target entity or process.
-
C.
primaryRainySeasonFor
Indicates that one entity is the main or most significant rainy season associated with a particular place or region.
-
D.
hasTropicalCyclones
Indicates that the specified region or area experiences tropical cyclones as part of its typical weather or climate conditions.
-
E.
containsMajorClimatePhenomenon
chosen
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_69ca831ee390819095fae73400bbfafc |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe57dbe488190af5f06faf862cd5d |
completed | March 31, 2026, 3:17 p.m. |
| PD | Predicate disambiguation | batch_69cbd10a4b0881909e254117780dc823 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:13 p.m.