Triple
T5436341
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Serang |
E122015
|
entity |
| Predicate | hasWetSeason |
P47435
|
FINISHED |
| Object | roughly November to 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: roughly November to April | Statement: [Serang, hasWetSeason, roughly November to April]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWetSeason Context triple: [Serang, hasWetSeason, roughly November to April]
-
A.
hasDrySeasonCause
Indicates that one factor or condition is the underlying cause of a location or region experiencing a dry season.
-
B.
wettestMonths
chosen
Indicates the months during which a location experiences the highest amount of precipitation compared to other months.
-
C.
drySeason
Indicates that the relationship or action occurs during, or is characteristic of, a period with little or no rainfall.
-
D.
primaryRainySeasonFor
Indicates that one entity is the main or most significant rainy season associated with a particular place or region.
-
E.
hasHotSeason
Indicates that an entity experiences a distinct period of time characterized by hot or high-temperature 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_69bd46400768819092925d461c0b8432 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd922f66bc8190b7d47fd68d2fcf2e |
completed | March 20, 2026, 6:30 p.m. |
| PD | Predicate disambiguation | batch_69bd919aeb048190b786f814177d6cd9 |
completed | March 20, 2026, 6:27 p.m. |
Created at: March 20, 2026, 2:07 p.m.