Triple
T9300408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aconcagua summit area |
E223743
|
entity |
| Predicate | extremeTemperature |
P17982
|
FINISHED |
| Object | can drop below −30 °C |
—
|
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: can drop below −30 °C | Statement: [Aconcagua summit area, extremeTemperature, can drop below −30 °C]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: extremeTemperature Context triple: [Aconcagua summit area, extremeTemperature, can drop below −30 °C]
-
A.
typicalTemperature
Indicates the usual or characteristic temperature associated with an entity under normal conditions.
-
B.
hasExtremeWeatherCharacteristic
chosen
Indicates that something possesses a notable or defining feature related to extreme weather conditions.
-
C.
maximumRecordedTemperature
Indicates the highest temperature value that has been observed and recorded for a given entity or context.
-
D.
averageTemperature
Indicates the typical or mean temperature value associated with an entity over a specified period or context.
-
E.
surfaceTemperatureRange
Indicates the range between the minimum and maximum surface temperatures observed or allowed for an entity.
- 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_69ca8423edb08190bc0c91287a484768 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd08d070c881908bed41aada6f85ae |
completed | April 1, 2026, noon |
| PD | Predicate disambiguation | batch_69cc7a5ef1908190bc5ca166bb895af6 |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:36 p.m.