Triple
T16145859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | City of Daytona Beach |
E391777
|
entity |
| Predicate | averageAnnualLowTemperature |
P121313
|
FINISHED |
| Object | about 63 °F |
—
|
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: about 63 °F | Statement: [City of Daytona Beach, averageAnnualLowTemperature, about 63 °F]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averageAnnualLowTemperature Context triple: [City of Daytona Beach, averageAnnualLowTemperature, about 63 °F]
-
A.
averageWinterLowTemperature
Indicates the typical minimum temperature experienced during the winter season for a given location or period.
-
B.
averageJanuaryLowTemperature
Indicates the typical minimum daily temperature experienced in a location during the month of January.
-
C.
averageMinTemperatureColdestMonth
Indicates the lowest average minimum temperature recorded during the coldest month in a given location or period.
-
D.
averageColdestMonth
Indicates the month in which an entity experiences the lowest average temperature over a given period.
-
E.
averageTemperature
Indicates the typical or mean temperature value associated with an entity over a specified period or context.
- F. None of above. chosen
Provenance (4 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_69d87f1c65e48190aa2b4c472e9bafc4 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e21d9376fc8190bd9ef586b00c1d3b |
completed | April 17, 2026, 11:46 a.m. |
| PD | Predicate disambiguation | batch_69e182885bc08190822ae7e8a4b8ac1f |
completed | April 17, 2026, 12:44 a.m. |
| PDg | Predicate description generation | batch_69e1835b64948190ae1d2a9d4cc64acf |
completed | April 17, 2026, 12:48 a.m. |
Created at: April 10, 2026, 5:01 a.m.