Triple
T20805276
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hurricane Ophelia (2005) |
E512137
|
entity |
| Predicate | rainfallEffect |
P69051
|
FINISHED |
| Object | heavy rain over coastal Carolinas |
—
|
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 rain over coastal Carolinas | Statement: [Hurricane Ophelia (2005), rainfallEffect, heavy rain over coastal Carolinas]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rainfallEffect Context triple: [Hurricane Ophelia (2005), rainfallEffect, heavy rain over coastal Carolinas]
-
A.
rainfallImpact
chosen
Indicates how rainfall influences or alters the condition, behavior, or outcome of a target entity or process.
-
B.
rainfallLevel
Indicates the amount or intensity of rainfall occurring at a given place and time.
-
C.
hasSignificantWeatherInfluence
Indicates that one entity exerts a substantial impact on the weather conditions or patterns experienced by another entity or region.
-
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.
lakeEffect
Indicates a weather phenomenon where a large lake modifies passing air masses, typically enhancing precipitation or altering local atmospheric conditions downwind of the lake.
- 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_69e0b4cc69f481908e98751e697b9df4 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2cf1cbc819092d92625dfb107d0 |
completed | April 21, 2026, 12:20 a.m. |
| PD | Predicate disambiguation | batch_69e5c99ca55481908e8d434fa901cfd6 |
completed | April 20, 2026, 6:37 a.m. |
Created at: April 16, 2026, 12:40 p.m.