Triple
T7133328
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Kelud |
E166242
|
entity |
| Predicate | impactOnRegion |
P25846
|
FINISHED |
| Object | agricultural damage in East Java |
—
|
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: agricultural damage in East Java | Statement: [Mount Kelud, impactOnRegion, agricultural damage in East Java]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactOnRegion Context triple: [Mount Kelud, impactOnRegion, agricultural damage in East Java]
-
A.
impactRegion
chosen
Indicates the geographic or spatial area that is affected or influenced by a particular event, action, or phenomenon.
-
B.
economicImpactRegion
Indicates the region or geographic area that experiences or is affected by a particular economic impact.
-
C.
influencesRegion
Indicates that one entity has an effect on, shapes, or alters the conditions, characteristics, or behavior of a specified region.
-
D.
regionOfCulturalImpact
Indicates the geographic area where an entity’s cultural influence, activities, or effects are most significantly felt or observed.
-
E.
nationalImpact
Indicates that something has a significant effect or influence at the level of an entire nation.
- 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_69c68884a9388190af42f90d1c1a7151 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e6703ad88190a498665500fee5a1 |
completed | March 27, 2026, 8:20 p.m. |
| PD | Predicate disambiguation | batch_69c6e1c7289881909f3b533c384f9ed4 |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:45 p.m.