Triple
T4943012
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Red Spot |
E110981
|
entity |
| Predicate | stormType |
P3932
|
FINISHED |
| Object | high-pressure system |
—
|
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: high-pressure system | Statement: [Great Red Spot, stormType, high-pressure system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: stormType Context triple: [Great Red Spot, stormType, high-pressure system]
-
A.
typicalStormType
chosen
Indicates the kind of storm that is most commonly or characteristically associated with a given context or location.
-
B.
stormedOn
Indicates that a storm or severe weather event occurred affecting or impacting a particular entity or location.
-
C.
associatedWithPrecipitationType
Indicates that there is a relationship between an entity and a specific type or category of precipitation (such as rain, snow, or hail).
-
D.
hasExtremeWeatherCharacteristic
Indicates that something possesses a notable or defining feature related to extreme weather conditions.
-
E.
hasNaturalPhenomenon
Indicates that a location, region, or environment possesses or is characterized by a particular natural phenomenon (such as a weather event, geological feature, or celestial occurrence).
- 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_69bd441721cc819085c7e33fe0876818 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd70a7650c8190b046b65072fd8eae |
completed | March 20, 2026, 4:07 p.m. |
| PD | Predicate disambiguation | batch_69bd6c389b9881908ad7fb1c5393c1b1 |
completed | March 20, 2026, 3:48 p.m. |
Created at: March 20, 2026, 1:31 p.m.