Triple
T5350359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kupang |
E124162
|
entity |
| Predicate | hasRainySeason |
P1014
|
FINISHED |
| Object | December–April |
—
|
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: December–April | Statement: [Kupang, hasRainySeason, December–April]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRainySeason Context triple: [Kupang, hasRainySeason, December–April]
-
A.
primaryRainySeasonFor
Indicates that one entity is the main or most significant rainy season associated with a particular place or region.
-
B.
hasDrySeasonCause
Indicates that one factor or condition is the underlying cause of a location or region experiencing a dry season.
-
C.
hasSeasonalNature
Indicates that something exhibits characteristics, behavior, or occurrence patterns that vary according to specific seasons or times of the year.
-
D.
hasHotSeason
Indicates that an entity experiences a distinct period of time characterized by hot or high-temperature weather conditions.
-
E.
hasSeason
chosen
Indicates that an entity possesses, occurs during, or is associated with a particular season or set of seasons.
- 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_69bd464be27081908807b40b75c1bbae |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd860fe4048190846a933d0e1b9386 |
completed | March 20, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69bd845c6f108190832a8d14b356368a |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2:01 p.m.