Triple
T12963542
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Af (Köppen) |
E321206
|
entity |
| Predicate | cloudCoverCharacteristic |
P54596
|
FINISHED |
| Object | frequent cloudiness |
—
|
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: frequent cloudiness | Statement: [Af (Köppen), cloudCoverCharacteristic, frequent cloudiness]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cloudCoverCharacteristic Context triple: [Af (Köppen), cloudCoverCharacteristic, frequent cloudiness]
-
A.
altitudeCoverageCharacteristic
Indicates the range or specific values of altitude over which something (such as a system, sensor, or service) is designed to operate or provide coverage.
-
B.
forestCoverCharacteristic
Indicates a relationship where a forested area possesses a specific attribute or quality related to its tree or vegetation cover.
-
C.
snowCover
Indicates that one entity is covered by or blanketed with snow.
-
D.
vegetationCoverage
Indicates the extent or proportion of an area that is covered by vegetation.
-
E.
summitOftenCloudCovered
chosen
Indicates that the summit of something is frequently or typically covered by clouds.
- 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_69d80763bd6c819094437da5b20b01d2 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e59a4c88190907d05b8d57dae89 |
completed | April 10, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69d97dba57988190b786ffed55687a72 |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 8:25 p.m.