Triple
T116180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Dividing Range |
E2342
|
entity |
| Predicate | affectsRainfallPatternsIn |
P3009
|
FINISHED |
| Object | eastern Australia |
—
|
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: eastern Australia | Statement: [Great Dividing Range, affectsRainfallPatternsIn, eastern Australia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: affectsRainfallPatternsIn Context triple: [Great Dividing Range, affectsRainfallPatternsIn, eastern Australia]
-
A.
hasClimateInfluence
chosen
Indicates that one entity affects or contributes to the climate characteristics or climate-related conditions of another entity.
-
B.
containsMajorClimatePhenomenon
Indicates that the subject region or area includes or experiences a significant, large-scale climate-related event or pattern.
-
C.
hasClimate
Indicates that an entity possesses or is characterized by a particular type of climate or climatic conditions.
-
D.
averageAnnualPrecipitation
Indicates the typical total amount of precipitation an entity receives over the course of a year, averaged across multiple years.
-
E.
affectsTaxon
Indicates that one entity has an impact or influence on a particular taxon or group of organisms.
- 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_69a2506c5428819085c28a8884790e29 |
completed | Feb. 28, 2026, 2:18 a.m. |
| NER | Named-entity recognition | batch_69a257845c548190bfb49409988d1c57 |
completed | Feb. 28, 2026, 2:48 a.m. |
| PD | Predicate disambiguation | batch_69a256456d908190b52c937fe6c4343f |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:24 a.m.