Triple
T13707337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Francisco Bay Area airspace system |
E328675
|
entity |
| Predicate | hasSignificantWeatherInfluence |
P111264
|
FINISHED |
| Object | marine layer fog |
—
|
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: marine layer fog | Statement: [San Francisco Bay Area airspace system, hasSignificantWeatherInfluence, marine layer fog]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSignificantWeatherInfluence Context triple: [San Francisco Bay Area airspace system, hasSignificantWeatherInfluence, marine layer fog]
-
A.
hasClimateInfluence
Indicates that one entity affects or contributes to the climate characteristics or climate-related conditions of another entity.
-
B.
hasSevereWeatherRisk
Indicates that an entity is exposed to or associated with a high likelihood of severe or hazardous weather conditions.
-
C.
hasExtremeWeatherCharacteristic
Indicates that something possesses a notable or defining feature related to extreme weather conditions.
-
D.
hasHighPrecipitation
Indicates that a location or time period experiences a large amount of precipitation, such as rain or snow, relative to a defined standard or average.
-
E.
containsMajorClimatePhenomenon
Indicates that the subject region or area includes or experiences a significant, large-scale climate-related event or pattern.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dcad18d48c8190a26be865c31975d6 |
completed | April 13, 2026, 8:45 a.m. |
| PD | Predicate disambiguation | batch_69dbbe92d77c81908e0244cffb7f78c5 |
completed | April 12, 2026, 3:47 p.m. |
| PDg | Predicate description generation | batch_69dbc59ca1a88190a6abd3bd00554c93 |
completed | April 12, 2026, 4:17 p.m. |
Created at: April 9, 2026, 9:54 p.m.