Triple
T3003491
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vaitogi |
E81842
|
entity |
| Predicate | hasNaturalHazardRisk |
P44599
|
FINISHED |
| Object | tropical cyclones |
—
|
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 cyclones | Statement: [Vaitogi, hasNaturalHazardRisk, tropical cyclones]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNaturalHazardRisk Context triple: [Vaitogi, hasNaturalHazardRisk, tropical cyclones]
-
A.
frequentNaturalHazard
Indicates that a location or area regularly experiences natural hazards such as floods, earthquakes, storms, or similar events with notable frequency.
-
B.
hasTsunamiRisk
Indicates that the subject is exposed to or associated with a potential risk of tsunamis.
-
C.
geologicalHazardZoneFor
Indicates a relationship where a specified area or zone is identified as being at risk from a particular geological hazard (such as earthquakes, landslides, or volcanic activity).
-
D.
earthquakeHazardLevel
Indicates the assessed degree of risk or potential impact from earthquakes associated with a given location or entity.
-
E.
hasFloodRisk
Indicates that an entity is exposed to a potential or expected risk of flooding under certain conditions.
- F. None of above. chosen
Provenance (4 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_69ad8b1c4de88190a83b7cefaa1f2842 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a1371c481909e214234afed1a65 |
completed | March 8, 2026, 3:47 p.m. |
| PD | Predicate disambiguation | batch_69ad96180eb08190a524c5f458d41382 |
completed | March 8, 2026, 3:30 p.m. |
| PDg | Predicate description generation | batch_69ad97f6af3881909f4547967384114c |
completed | March 8, 2026, 3:38 p.m. |
Created at: March 8, 2026, 2:59 p.m.