Triple
T19706110
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruka Pillan |
E473217
|
entity |
| Predicate | hasNaturalHazardType |
P1950
|
FINISHED |
| Object | volcanic eruptions |
—
|
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: volcanic eruptions | Statement: [Ruka Pillan, hasNaturalHazardType, volcanic eruptions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNaturalHazardType Context triple: [Ruka Pillan, hasNaturalHazardType, volcanic eruptions]
-
A.
hasNaturalHazardRisk
Indicates that an entity is exposed or subject to potential damage or impact from one or more natural hazards (e.g., earthquakes, floods, storms).
-
B.
frequentNaturalHazard
Indicates that a location or area regularly experiences natural hazards such as floods, earthquakes, storms, or similar events with notable frequency.
-
C.
hasNaturalPhenomenon
Indicates that a location, region, or environment possesses or is characterized by a particular natural phenomenon (such as a weather event, geological feature, or celestial occurrence).
-
D.
hazardType
chosen
Indicates the specific kind or category of hazard associated with an entity or situation.
-
E.
geologicHazardLevel
Indicates the degree of potential danger or risk posed by geologic processes or conditions at a given location.
- 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_69d8e516dd048190a0b6c93ea3e71f58 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e642ba4bd08190b016b8c6664079c4 |
completed | April 20, 2026, 3:14 p.m. |
| PD | Predicate disambiguation | batch_69e530438c60819082364c7be3eef6f0 |
completed | April 19, 2026, 7:42 p.m. |
Created at: April 10, 2026, 1:46 p.m.