Triple
T6564424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sorrow of Bihar |
E153865
|
entity |
| Predicate | disasterCategory |
P64317
|
FINISHED |
| Object | recurrent natural disaster source |
—
|
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: recurrent natural disaster source | Statement: [Sorrow of Bihar, disasterCategory, recurrent natural disaster source]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: disasterCategory Context triple: [Sorrow of Bihar, disasterCategory, recurrent natural disaster source]
-
A.
notableDisasterType
chosen
Indicates the specific kind or category of disaster for which something (such as a place, event, or entity) is notable or best known.
-
B.
disasterDepicted
Indicates that one entity visually represents or portrays a disaster involving or affecting another entity.
-
C.
supportsDisasterType
Indicates that one entity is capable of handling, responding to, or being applicable to a specified type of disaster.
-
D.
hasDisaster
Indicates that an entity experiences, is affected by, or is associated with a disaster event.
-
E.
frequentNaturalHazard
Indicates that a location or area regularly experiences natural hazards such as floods, earthquakes, storms, or similar events with notable frequency.
- 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_69c6880cb35881909b763eb0125236b9 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6cc9c6cb0819084fec8e0beb430de |
completed | March 27, 2026, 6:29 p.m. |
| PD | Predicate disambiguation | batch_69c6acf93cb48190b54f5dd6febd34dc |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:52 p.m.