Triple
T19081209
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miracle of the Sun |
E467035
|
entity |
| Predicate | hasWeatherContext |
P134282
|
FINISHED |
| Object | event followed a period of rain |
—
|
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: event followed a period of rain | Statement: [Miracle of the Sun, hasWeatherContext, event followed a period of rain]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWeatherContext Context triple: [Miracle of the Sun, hasWeatherContext, event followed a period of rain]
-
A.
hasWeather
Indicates that a location or environment is experiencing or characterized by a particular type of weather condition.
-
B.
canProvideWeatherInformation
Indicates that an entity has the capability to supply or answer queries about weather-related data or conditions.
-
C.
allWeatherCapability
Indicates that an entity is capable of operating effectively under all weather conditions.
-
D.
weatherCondition
Indicates the type of atmospheric state or weather pattern (e.g., sunny, rainy, snowy) affecting a location or time period.
-
E.
hasClimateContext
Indicates that something is associated with, influenced by, or relevant to climate-related conditions, factors, or considerations.
- 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_69d8dd04f4488190b1121cc53ef2bfd6 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e2e8f8148190942cca6dd3e30caf |
completed | April 20, 2026, 8:25 a.m. |
| PD | Predicate disambiguation | batch_69e4b9a604308190a3235184f9f2c056 |
completed | April 19, 2026, 11:16 a.m. |
| PDg | Predicate description generation | batch_69e4bfe8a06081909fd5c28a33e9f218 |
completed | April 19, 2026, 11:43 a.m. |
Created at: April 10, 2026, 12:04 p.m.