Triple
T37649452
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Storm Eva |
E937131
|
entity |
| Predicate | hasWeatherPhenomenon |
P2044
|
FINISHED |
| Object | heavy rainfall |
—
|
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: heavy rainfall | Statement: [Storm Eva, hasWeatherPhenomenon, heavy rainfall]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWeatherPhenomenon Context triple: [Storm Eva, hasWeatherPhenomenon, heavy rainfall]
-
A.
hasWinterPhenomenon
Indicates that an entity experiences or is characterized by a particular phenomenon occurring during the winter season.
-
B.
hasWeather
chosen
Indicates that a location or environment is experiencing or characterized by a particular type of weather condition.
-
C.
hasPrecipitationCriterion
Indicates that something is subject to, defined by, or must satisfy a specified condition related to precipitation (such as amount, type, or occurrence of rainfall, snow, etc.).
-
D.
weatherCapability
Indicates that an entity has the ability or functionality to provide, process, or otherwise handle weather-related information or services.
-
E.
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.
- 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_69f76ed4fe908190b8061c5c135e0971 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcc4b700748190ae00b21d09c96695 |
completed | May 7, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_69fcb0f9d3d881908a049475182fb039 |
completed | May 7, 2026, 3:34 p.m. |
Created at: May 3, 2026, 4:18 p.m.