Triple
T1049170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dark Peak |
E22653
|
entity |
| Predicate | dominantLandscape |
P9701
|
FINISHED |
| Object | moorland |
—
|
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: moorland | Statement: [Dark Peak, dominantLandscape, moorland]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dominantLandscape Context triple: [Dark Peak, dominantLandscape, moorland]
-
A.
landscapeType
chosen
Indicates the kind or category of natural terrain or scenery that characterizes a place or area.
-
B.
landscapeStyle
Indicates the design style or aesthetic approach applied to a landscape or outdoor environment.
-
C.
dominantRegion
Indicates that one region exerts primary control, influence, or prominence over another region within a given context.
-
D.
hasLandscapeFeatures
Indicates that an entity possesses or includes specific landscape-related characteristics or elements.
-
E.
primaryLandUse
Indicates the main or dominant way in which a given piece of land is utilized or designated (e.g., residential, agricultural, commercial).
- 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_69a493da02e081908c13ff5e02a0fe7a |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b8f28c7c8190b9ca3749666bcbf3 |
completed | March 1, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69a4b72e3e488190b768005ad647886b |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.