Triple
T14963766
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chino Valley, Arizona |
E373132
|
entity |
| Predicate | averageSummerHighFahrenheit |
P79363
|
FINISHED |
| Object | 90 |
—
|
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: 90 | Statement: [Chino Valley, Arizona, averageSummerHighFahrenheit, 90]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averageSummerHighFahrenheit Context triple: [Chino Valley, Arizona, averageSummerHighFahrenheit, 90]
-
A.
averageSummerHighTemperatureF
chosen
Indicates the typical maximum daily air temperature, measured in degrees Fahrenheit, during the summer season for a given location or period.
-
B.
averageSummerTemperatureC
Indicates the typical or mean air temperature, measured in degrees Celsius, experienced at a location during the summer season.
-
C.
averageWarmestMonth
Indicates the relationship between a place and the month in which its long-term average temperature is highest.
-
D.
averageTemperature
Indicates the typical or mean temperature value associated with an entity over a specified period or context.
-
E.
averageHighTemperatureInJuly
Indicates the typical or mean value of the highest daily temperatures recorded during the month of July for a given location.
- 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_69d85cca979481908747d2a81eba1cea |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6d0487c8190b7754af8c5014b37 |
completed | April 15, 2026, 12:07 a.m. |
| PD | Predicate disambiguation | batch_69de9a5d995881909e33658f5aea5582 |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:40 a.m.