Triple
T26837840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luge at the 2014 Winter Olympics |
E675685
|
entity |
| Predicate | trackVerticalDrop |
P128997
|
FINISHED |
| Object | 129 m |
—
|
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: 129 m | Statement: [Luge at the 2014 Winter Olympics, trackVerticalDrop, 129 m]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trackVerticalDrop Context triple: [Luge at the 2014 Winter Olympics, trackVerticalDrop, 129 m]
-
A.
verticalDrop_ft
Indicates the vertical distance, measured in feet, that one entity drops or falls relative to another reference level.
-
B.
skiVerticalDrop
Indicates the vertical distance in elevation from the top to the bottom of a ski run or ski area.
-
C.
verticalDrop_m
chosen
Indicates the vertical distance, measured in meters, through which something drops or falls from a higher point to a lower point.
-
D.
hasSkiAreaVerticalDrop
Indicates the vertical distance in elevation between the highest and lowest points of a ski area.
-
E.
tallestSingleDrop
Indicates that one entity has the greatest single uninterrupted vertical drop (e.g., in height or distance) compared to all others in a given context.
- 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_69eee9b776448190993a60b67fcc9545 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f63fd6c68481908c542aa03e297b9c |
completed | May 2, 2026, 6:17 p.m. |
| PD | Predicate disambiguation | batch_69f63c663be481908f233d25d28713a4 |
completed | May 2, 2026, 6:03 p.m. |
Created at: April 27, 2026, 5:05 a.m.