Triple
T27916366
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montañas del Fuego |
E706084
|
entity |
| Predicate | landscapeDescription |
P163415
|
FINISHED |
| Object | otherworldly |
—
|
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: otherworldly | Statement: [Montañas del Fuego, landscapeDescription, otherworldly]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: landscapeDescription Context triple: [Montañas del Fuego, landscapeDescription, otherworldly]
-
A.
scenicDescription
Indicates a descriptive portrayal of the visual or aesthetic qualities of a scene or landscape.
-
B.
landscapeType
Indicates the kind or category of natural terrain or scenery that characterizes a place or area.
-
C.
landscapeStyle
Indicates the design style or aesthetic approach applied to a landscape or outdoor environment.
-
D.
landscapeElement
Indicates that one entity functions as a landscape-related feature or component in relation to another entity.
-
E.
landscapeAppearance
chosen
Indicates how a landscape visually presents itself, such as its overall look, style, or aesthetic qualities.
- 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_69ef96b6cc808190aab19fb18b235f4b |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69f643ed0b7481908cf25f3afec0a61d |
completed | May 2, 2026, 6:35 p.m. |
| PD | Predicate disambiguation | batch_69f641def1e88190a05bf865ced78b23 |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 27, 2026, 6:53 p.m.