Triple
T14747932
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vertical Kilometer du Mont-Blanc |
E346521
|
entity |
| Predicate | verticalGain |
P10733
|
FINISHED |
| Object | approximately 1000 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: approximately 1000 metres | Statement: [Vertical Kilometer du Mont-Blanc, verticalGain, approximately 1000 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: verticalGain Context triple: [Vertical Kilometer du Mont-Blanc, verticalGain, approximately 1000 metres]
-
A.
verticalExtent
chosen
Indicates the total height or vertical span of an entity or region from its lowest to highest point.
-
B.
verticalLocation
Indicates a vertical positional relationship where one entity is located above or below another along the up-down axis.
-
C.
targetVertical
Indicates that one entity is directed toward, aligned with, or intended for a specific vertical market, domain, or industry segment.
-
D.
verticalDrop_ft
Indicates the vertical distance, measured in feet, that one entity drops or falls relative to another reference level.
-
E.
verticalOrder
Indicates that one entity is positioned directly above or below another along a vertical axis, establishing their relative vertical arrangement.
- 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_69d822e6f1c88190bc494d491a907114 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7d116e88190828b163b18d80f68 |
completed | April 14, 2026, 11:03 p.m. |
| PD | Predicate disambiguation | batch_69de8bf9331481909582045cd567d91f |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:30 a.m.