Triple
T17986655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Bandai |
E430252
|
entity |
| Predicate | landscapeEffect |
P130020
|
FINISHED |
| Object | caused massive debris avalanches in 1888 |
—
|
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: caused massive debris avalanches in 1888 | Statement: [Mount Bandai, landscapeEffect, caused massive debris avalanches in 1888]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: landscapeEffect Context triple: [Mount Bandai, landscapeEffect, caused massive debris avalanches in 1888]
-
A.
landscapeAbstraction
Indicates that one entity is an abstract or non-literal representation derived from the landscape or its features.
-
B.
landscapeStyle
Indicates the design style or aesthetic approach applied to a landscape or outdoor environment.
-
C.
resolutionEffect
Indicates the outcome, consequence, or change that results from a particular resolution, decision, or problem-solving action.
-
D.
visualEffect
Indicates that one entity produces, modifies, or is associated with a particular visual effect on another entity or within a scene.
-
E.
landscapeElement
Indicates that one entity functions as a landscape-related feature or component in relation to another entity.
- 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_69d8b90364248190a37381adea932f42 |
completed | April 10, 2026, 8:46 a.m. |
| NER | Named-entity recognition | batch_69e4b29c6d0881909d450d2561d532a6 |
completed | April 19, 2026, 10:46 a.m. |
| PD | Predicate disambiguation | batch_69e3f8fa62688190a5d5c361ab896256 |
completed | April 18, 2026, 9:34 p.m. |
| PDg | Predicate description generation | batch_69e42d8d68288190a05dc5d7803cf823 |
completed | April 19, 2026, 1:19 a.m. |
Created at: April 10, 2026, 10:23 a.m.