Triple
T31714670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rockport, Texas |
E809420
|
entity |
| Predicate | frequentWeatherEvent |
P25357
|
FINISHED |
| Object | tropical storms |
—
|
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: tropical storms | Statement: [Rockport, Texas, frequentWeatherEvent, tropical storms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: frequentWeatherEvent Context triple: [Rockport, Texas, frequentWeatherEvent, tropical storms]
-
A.
hasExtremeWeatherCharacteristic
Indicates that something possesses a notable or defining feature related to extreme weather conditions.
-
B.
climateEventType
Indicates the specific kind or category of a climate-related event associated with an entity or occurrence.
-
C.
typicalWeatherFeature
Indicates a weather condition or pattern that commonly characterizes a place or time period.
-
D.
frequentNaturalHazard
chosen
Indicates that a location or area regularly experiences natural hazards such as floods, earthquakes, storms, or similar events with notable frequency.
-
E.
stormCategory
Indicates the classification level or intensity category assigned to a storm based on its measured strength or impact.
- 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_69f348df4e048190a4a5a9932ada78d6 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6c1bb5f248190834161b5a6ba1ece |
completed | May 3, 2026, 3:32 a.m. |
| PD | Predicate disambiguation | batch_69f6bd25bed08190befcabd3a41ffadf |
completed | May 3, 2026, 3:12 a.m. |
Created at: April 30, 2026, 11:16 p.m.