Triple
T22435046
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bionnassay–Mont Blanc traverse |
E554598
|
entity |
| Predicate | hasDifficultySystem |
P144831
|
FINISHED |
| Object | Alpine grading system |
—
|
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: Alpine grading system | Statement: [Bionnassay–Mont Blanc traverse, hasDifficultySystem, Alpine grading system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDifficultySystem Context triple: [Bionnassay–Mont Blanc traverse, hasDifficultySystem, Alpine grading system]
-
A.
hasDifficultyClass
Indicates that something (such as a task, challenge, or problem) is associated with a specific level of difficulty or complexity.
-
B.
difficultySystem
Indicates a relationship where a system is characterized or classified by its level of difficulty.
-
C.
hasDifficultyEffect
Indicates that one entity causes a change in the difficulty level or challenge associated with another entity or activity.
-
D.
hasDifficultyContext
Indicates that something’s difficulty is defined, interpreted, or constrained within a particular situational or contextual framework.
-
E.
difficultyGradeSystem
chosen
Indicates a system that defines or categorizes the level of difficulty associated with tasks, problems, or items.
- 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_69e11e5010e48190ae1e9c9db9697637 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15adda0e48190825a5b705ae52d5b |
completed | April 29, 2026, 1:11 a.m. |
| PD | Predicate disambiguation | batch_69e898a327948190beee5e168006a0a7 |
completed | April 22, 2026, 9:45 a.m. |
Created at: April 16, 2026, 8:47 p.m.