Triple
T11224606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Knife Edge arête |
E265661
|
entity |
| Predicate | safetyAdvice |
P97924
|
FINISHED |
| Object | avoid in bad weather or high winds |
—
|
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: avoid in bad weather or high winds | Statement: [Knife Edge arête, safetyAdvice, avoid in bad weather or high winds]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyAdvice Context triple: [Knife Edge arête, safetyAdvice, avoid in bad weather or high winds]
-
A.
safetyCategory
Indicates the classification of something according to its level or type of safety.
-
B.
safety
Indicates that an entity provides, ensures, or is associated with protection from harm, danger, or risk for another entity or within a given context.
-
C.
safetyRationale
Indicates the reasoning or justification provided to explain how and why something is considered safe or made safe.
-
D.
safetyConcept
Indicates that something embodies, represents, or is associated with a principle, idea, or framework related to safety.
-
E.
safetyPoints
Indicates a relationship where an entity is assigned or associated with a measure of safety, typically quantified as points reflecting its safety level or performance.
- F. None of above. chosen
Provenance (4 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_69d6aac59460819089b9848b27f57848 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8ee15d4819087449058addef597 |
completed | April 9, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69d75cfbbb188190861efd5d94fe27da |
completed | April 9, 2026, 8:02 a.m. |
| PDg | Predicate description generation | batch_69d77062271c8190b63da714ab5beff9 |
completed | April 9, 2026, 9:24 a.m. |
Created at: April 8, 2026, 9:30 p.m.