Triple
T4543041
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Calbuco Volcano |
E109980
|
entity |
| Predicate | hasPhotoFeature |
P57608
|
FINISHED |
| Object | distinctive ash columns during 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: distinctive ash columns during eruptions | Statement: [Calbuco Volcano, hasPhotoFeature, distinctive ash columns during eruptions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPhotoFeature Context triple: [Calbuco Volcano, hasPhotoFeature, distinctive ash columns during eruptions]
-
A.
hasAIPhotoFeatures
Indicates that an entity provides or supports photo-related features powered by artificial intelligence.
-
B.
hasCamera
Indicates that an entity is equipped with or possesses a camera.
-
C.
hasPhotoSpot
Indicates that a location or entity includes or is associated with a designated place suitable for taking photographs.
-
D.
hasPhotograph
Indicates that one entity possesses, includes, or is associated with a photograph depicting or representing another entity.
-
E.
hasOuterSelfieCamera
Indicates that an entity (typically a device) is equipped with a front-facing camera intended for taking selfies.
- 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_69bd4412524c8190be5bcc9ddee91848 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57d3be988190bf118c4a87415613 |
completed | March 20, 2026, 2:21 p.m. |
| PD | Predicate disambiguation | batch_69bd5220e40481908ca2d7e2c43d8531 |
completed | March 20, 2026, 1:56 p.m. |
| PDg | Predicate description generation | batch_69bd56f6e75481909c487a94a2c2d0ba |
completed | March 20, 2026, 2:17 p.m. |
Created at: March 20, 2026, 1:05 p.m.