Triple
T427456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | K2 |
E9638
|
entity |
| Predicate | rankingByElevation |
P2472
|
FINISHED |
| Object | Second-highest mountain in the world |
—
|
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: Second-highest mountain in the world | Statement: [K2, rankingByElevation, Second-highest mountain in the world]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingByElevation Context triple: [K2, rankingByElevation, Second-highest mountain in the world]
-
A.
rankByHeightWorld
chosen
Indicates an ordering of entities based on their relative height compared to all others in the world.
-
B.
highestPoint
Indicates that one entity is the point with the greatest elevation or height relative to another entity or defined area.
-
C.
rankInNorthAmericaByElevation
Indicates the relative position of a place in an ordered list of locations in North America based on their elevation.
-
D.
elevation
Indicates the vertical height or altitude of one entity relative to a reference level or another entity.
-
E.
elevationType
Indicates the kind or classification of elevation associated with an entity, such as how its height or altitude is characterized.
- 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_69a2e801e1d48190b505d1dd336b52ac |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2eed7f3508190995dcd39586ed614 |
completed | Feb. 28, 2026, 1:34 p.m. |
| PD | Predicate disambiguation | batch_69a2edd7a3608190b8785c7b7205f6c1 |
completed | Feb. 28, 2026, 1:29 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.