Triple
T7157669
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Maracaibo |
E166855
|
entity |
| Predicate | climatePhenomenon |
P1886
|
FINISHED |
| Object | persistent nocturnal thunderstorms |
—
|
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: persistent nocturnal thunderstorms | Statement: [Lake Maracaibo, climatePhenomenon, persistent nocturnal thunderstorms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: climatePhenomenon Context triple: [Lake Maracaibo, climatePhenomenon, persistent nocturnal thunderstorms]
-
A.
hasExtremeWeatherCharacteristic
Indicates that something possesses a notable or defining feature related to extreme weather conditions.
-
B.
containsMajorClimatePhenomenon
chosen
Indicates that the subject region or area includes or experiences a significant, large-scale climate-related event or pattern.
-
C.
climateBetween
Indicates that something (such as a location, period, or condition) has a climate that lies within a specified range or intermediate state between two other climatic conditions.
-
D.
climatologicalType
Indicates the classification of a climate or weather pattern that characterizes a place, period, or condition.
-
E.
climateDriver
Indicates a factor or process that significantly influences or drives changes in climate conditions.
- 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_69c68887a5cc8190bec0ea96227164f7 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e80f14808190907ee84523630d85 |
completed | March 27, 2026, 8:26 p.m. |
| PD | Predicate disambiguation | batch_69c6e1cd5c948190a9113b23f7308c21 |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:47 p.m.