Triple
T23884923
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kearney Regional Airport |
E600302
|
entity |
| Predicate | hasWeatherServices |
P33698
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Kearney Regional Airport, hasWeatherServices, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWeatherServices Context triple: [Kearney Regional Airport, hasWeatherServices, yes]
-
A.
hasOnlineForecastService
Indicates that an entity provides an online service through which users can access forecasts.
-
B.
hasWeatherDepartment
Indicates that an entity maintains or includes a dedicated department or unit responsible for weather-related activities or services.
-
C.
canProvideWeatherInformation
chosen
Indicates that an entity has the capability to supply or answer queries about weather-related data or conditions.
-
D.
hasWeather
Indicates that a location or environment is experiencing or characterized by a particular type of weather condition.
-
E.
hasWeatherContext
Indicates that something is associated with, influenced by, or described in terms of specific weather conditions or patterns.
- 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_69e295318e148190b9979d8fc02e168f |
completed | April 17, 2026, 8:16 p.m. |
| NER | Named-entity recognition | batch_69f1ccfd99d481908aae44b387853c7d |
completed | April 29, 2026, 9:18 a.m. |
| PD | Predicate disambiguation | batch_69f1614e24b48190a1c8fb5b7c75ee0f |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 8:24 p.m.