Triple
T15922034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Col du Galibier |
E386114
|
entity |
| Predicate | maxGradientApprox |
P46516
|
FINISHED |
| Object | 10.1% |
—
|
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: 10.1% | Statement: [Col du Galibier, maxGradientApprox, 10.1%]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maxGradientApprox Context triple: [Col du Galibier, maxGradientApprox, 10.1%]
-
A.
maximumGradient
chosen
Indicates the greatest rate of change or steepest slope that occurs within a given function, surface, or dataset.
-
B.
averageGradient
Indicates the mean rate of change (slope) of a quantity over a specified interval or region.
-
C.
acceleratingGradient
Indicates that a process, change, or effect is increasing in rate over time, becoming progressively faster or more intense.
-
D.
approximates
Indicates that one entity is close to, but not exactly equal to, the value, form, or behavior of another entity.
-
E.
approximationType
Indicates the specific method or scheme used to approximate a value, function, or relationship 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_69d86da686e4819097cbf3b1fc2d881d |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e172b48b308190bc430b2308cbc75b |
completed | April 16, 2026, 11:37 p.m. |
| PD | Predicate disambiguation | batch_69e142cf5c548190a931f7b58144cd31 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:52 a.m.