Triple
T7706759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fort Yukon |
E174637
|
entity |
| Predicate | recordedExtremeTemperature |
P15651
|
FINISHED |
| Object | −78 °F (−61 °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: −78 °F (−61 °C) | Statement: [Fort Yukon, recordedExtremeTemperature, −78 °F (−61 °C)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: recordedExtremeTemperature Context triple: [Fort Yukon, recordedExtremeTemperature, −78 °F (−61 °C)]
-
A.
maximumRecordedTemperature
Indicates the highest temperature value that has been observed and recorded for a given entity or context.
-
B.
recordLowTemperature
chosen
Indicates that a specified temperature value is the lowest recorded temperature for a given entity, location, or time period.
-
C.
recordHighTemperatureLocation
Indicates the location where the highest recorded temperature occurred.
-
D.
notableWeather
Indicates that a location experiences weather conditions that are significant, unusual, or noteworthy in some way.
-
E.
snowfallRecord
Indicates that a specific amount of snow has been measured or documented for a particular place and time.
- 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_69c6995b3e8c8190833108f883d5f53c |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702ebb7448190ae8d47fe0cbb0907 |
completed | March 27, 2026, 10:21 p.m. |
| PD | Predicate disambiguation | batch_69c701683dec8190be9861e592aa8ce0 |
completed | March 27, 2026, 10:15 p.m. |
Created at: March 27, 2026, 4:04 p.m.