Triple
T32288536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Agadir beach |
E824899
|
entity |
| Predicate | hasAverageWeatherCharacteristic |
P132755
|
FINISHED |
| Object | plenty of sunshine |
—
|
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: plenty of sunshine | Statement: [Agadir beach, hasAverageWeatherCharacteristic, plenty of sunshine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAverageWeatherCharacteristic Context triple: [Agadir beach, hasAverageWeatherCharacteristic, plenty of sunshine]
-
A.
hasExtremeWeatherCharacteristic
Indicates that something possesses a notable or defining feature related to extreme weather conditions.
-
B.
typicalWeatherFeature
chosen
Indicates a weather condition or pattern that commonly characterizes a place or time period.
-
C.
hasClimate
Indicates that an entity possesses or is characterized by a particular type of climate or climatic conditions.
-
D.
hasWinterPhenomenon
Indicates that an entity experiences or is characterized by a particular phenomenon occurring during the winter season.
-
E.
winterCharacteristic
Indicates a characteristic, feature, or quality that is specifically associated with or typical of winter.
- 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_69f349101b788190b4f14884dc7d1ed2 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6bd311adc8190839fa2f9bb2e727d |
completed | May 3, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69f6b632cf788190a3d0c08cd026b84b |
completed | May 3, 2026, 2:42 a.m. |
Created at: May 1, 2026, 12:44 a.m.