Triple
T10103276
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Galunggung |
E216254
|
entity |
| Predicate | ashCloudEffect |
P92439
|
FINISHED |
| Object | multiple jet engine flameouts on aircraft |
—
|
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: multiple jet engine flameouts on aircraft | Statement: [Galunggung, ashCloudEffect, multiple jet engine flameouts on aircraft]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ashCloudEffect Context triple: [Galunggung, ashCloudEffect, multiple jet engine flameouts on aircraft]
-
A.
visualEffect
Indicates that one entity produces, modifies, or is associated with a particular visual effect on another entity or within a scene.
-
B.
fireEffect
Indicates that one entity produces, causes, or is associated with a fire-related impact or consequence on another entity.
-
C.
projectionEffect
Indicates the visual or spatial transformation produced when something is projected from one surface, medium, or viewpoint onto another.
-
D.
transparencyEffects
Indicates how the level or presence of transparency in one entity influences the perception, behavior, or properties of another entity.
-
E.
breathEffect
Indicates an effect or change that occurs as a direct result of an entity’s breath.
- 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_69ca83d039f08190b9d10363221c69fb |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cdd09af07c819099774af46ebf62d7 |
completed | April 2, 2026, 2:12 a.m. |
| PD | Predicate disambiguation | batch_69cd4b9b853c8190a2af993ce9b21309 |
completed | April 1, 2026, 4:45 p.m. |
| PDg | Predicate description generation | batch_69cd5150ae98819086c4f822114b4e2c |
completed | April 1, 2026, 5:09 p.m. |
Created at: March 30, 2026, 9:03 p.m.