Triple
T14907822
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Levante |
E371180
|
entity |
| Predicate | typicalWeatherImpact |
P32056
|
FINISHED |
| Object | clear skies on Spanish Mediterranean coast |
—
|
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: clear skies on Spanish Mediterranean coast | Statement: [Levante, typicalWeatherImpact, clear skies on Spanish Mediterranean coast]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalWeatherImpact Context triple: [Levante, typicalWeatherImpact, clear skies on Spanish Mediterranean coast]
-
A.
rainfallImpact
Indicates how rainfall influences or alters the condition, behavior, or outcome of a target entity or process.
-
B.
typicalPrecipitationPattern
Indicates the usual or characteristic pattern of precipitation associated with a place, time period, or climate condition.
-
C.
hasSignificantWeatherInfluence
Indicates that one entity exerts a substantial impact on the weather conditions or patterns experienced by another entity or region.
-
D.
typicalWindStrength
Indicates the usual or characteristic intensity of wind associated with something, such as a place, time, or condition.
-
E.
associatedWithWeather
chosen
Indicates a relationship where something is connected or related to weather conditions or phenomena.
- 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_69d85cc7ea3481908228b5acb7d06f12 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded61b3c808190b4f6df4e5cb401ad |
completed | April 15, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69de9a4a14a88190951bb8f4c60bd37b |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:23 a.m.