Triple
T15873599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paro Airport |
E384889
|
entity |
| Predicate | maxSurroundingMountainHeight |
P87793
|
FINISHED |
| Object | over 5000 metres |
—
|
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: over 5000 metres | Statement: [Paro Airport, maxSurroundingMountainHeight, over 5000 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maxSurroundingMountainHeight Context triple: [Paro Airport, maxSurroundingMountainHeight, over 5000 metres]
-
A.
maximumMoundHeight
chosen
Indicates the greatest allowable or observed height of a mound relative to a specified reference or context.
-
B.
adjacentMountain
Indicates that one mountain is directly next to or touching another mountain, with no significant separation between them.
-
C.
numberOfMountains
Indicates the quantitative count of mountains associated with a given entity or context.
-
D.
endPointMountain
Indicates that a path, route, or boundary terminates at a mountain.
-
E.
hasNearestMountain
Indicates that one entity is the closest mountain geographically relative to another entity.
- 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_69d86da4e86481909f1325fdc971b5ec |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e174de2cd48190ab18e48c9f051a2a |
completed | April 16, 2026, 11:46 p.m. |
| PD | Predicate disambiguation | batch_69e142c3e18c8190bb7b023f4a0eaebb |
completed | April 16, 2026, 8:12 p.m. |
Created at: April 10, 2026, 4:51 a.m.