Triple
T567408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fairbanks |
E13584
|
entity |
| Predicate | averageWinterTemperature |
P10789
|
FINISHED |
| Object | well below freezing |
—
|
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: well below freezing | Statement: [Fairbanks, averageWinterTemperature, well below freezing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averageWinterTemperature Context triple: [Fairbanks, averageWinterTemperature, well below freezing]
-
A.
averageTemperature
Indicates the typical or mean temperature value associated with an entity over a specified period or context.
-
B.
hasAverageSpringTemperature
Indicates that an entity is associated with a specific average temperature value measured over the spring season.
-
C.
averageHighTemperatureInJanuary
Indicates the typical or mean value of the highest daily temperatures recorded during the month of January for a given location.
-
D.
winterCharacteristic
chosen
Indicates a characteristic, feature, or quality that is specifically associated with or typical of winter.
-
E.
averageAnnualSnowfall
Indicates the typical amount of snow that falls in a given location over the course of a year, averaged across multiple years.
- 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_69a4933edcf08190b35ecfd6014caee6 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49b02da148190b6a9bad3a22d8ec5 |
completed | March 1, 2026, 8:01 p.m. |
| PD | Predicate disambiguation | batch_69a494c183b081909304944aa3d0fe8f |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:33 p.m.