Triple
T5856929
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tropical rain forest |
E130177
|
entity |
| Predicate | typicalAnnualRainfall |
P472
|
FINISHED |
| Object | 1500–3000 millimeters |
—
|
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: 1500–3000 millimeters | Statement: [Tropical rain forest, typicalAnnualRainfall, 1500–3000 millimeters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalAnnualRainfall Context triple: [Tropical rain forest, typicalAnnualRainfall, 1500–3000 millimeters]
-
A.
averageAnnualPrecipitation
chosen
Indicates the typical total amount of precipitation an entity receives over the course of a year, averaged across multiple years.
-
B.
averageAnnualSunshineDays
Indicates the typical number of days per year that a location experiences sunshine, averaged over a specified period.
-
C.
averageAnnualSnowfall
Indicates the typical amount of snow that falls in a given location over the course of a year, averaged across multiple years.
-
D.
rainfallPeak
Indicates the time or value at which rainfall intensity reaches its maximum during a given period or event.
-
E.
averageTemperature
Indicates the typical or mean temperature value associated with an entity over a specified period or context.
- 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_69c0084f3bb08190a7720f55f7aa4252 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c044ab0a048190b84be40fb13c0f50 |
completed | March 22, 2026, 7:36 p.m. |
| PD | Predicate disambiguation | batch_69c03345ca0c819081c81148d054fed2 |
completed | March 22, 2026, 6:21 p.m. |
Created at: March 22, 2026, 3:56 p.m.