Triple
T21385902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Groves, Texas |
E527495
|
entity |
| Predicate | subjectToWeatherEvent |
P14395
|
FINISHED |
| Object | hurricanes |
—
|
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: hurricanes | Statement: [Groves, Texas, subjectToWeatherEvent, hurricanes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectToWeatherEvent Context triple: [Groves, Texas, subjectToWeatherEvent, hurricanes]
-
A.
associatedWithWeather
Indicates a relationship where something is connected or related to weather conditions or phenomena.
-
B.
hasSignificantWeatherInfluence
Indicates that one entity exerts a substantial impact on the weather conditions or patterns experienced by another entity or region.
-
C.
hasSevereWeatherRisk
chosen
Indicates that an entity is exposed to or associated with a high likelihood of severe or hazardous weather conditions.
-
D.
hasWeather
Indicates that a location or environment is experiencing or characterized by a particular type of weather condition.
-
E.
hasMinimumWeatherRequirements
Indicates that a subject is associated with the lowest acceptable set of weather conditions required for a particular activity, operation, or state to occur.
- 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_69e0b51f363c8190944000ab5523b02b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b0f3d37c8190b43ec77cdb1904c8 |
completed | April 22, 2026, 11:28 a.m. |
| PD | Predicate disambiguation | batch_69e6162bbfc88190a3e75859941b2638 |
completed | April 20, 2026, 12:03 p.m. |
Created at: April 16, 2026, 5:12 p.m.