Triple
T14058484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlotte Pass Hotel |
E338279
|
entity |
| Predicate | snowAccess |
P112662
|
FINISHED |
| Object | frequent snow cover in winter |
—
|
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: frequent snow cover in winter | Statement: [Charlotte Pass Hotel, snowAccess, frequent snow cover in winter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: snowAccess Context triple: [Charlotte Pass Hotel, snowAccess, frequent snow cover in winter]
-
A.
snowQuality
Indicates the condition or characteristics of the snow, such as its texture, depth, or suitability for a particular use.
-
B.
snowLine
Indicates the altitude or boundary on a mountain or region above which snow persists year-round or seasonally.
-
C.
snowCover
Indicates that one entity is covered by or blanketed with snow.
-
D.
snowRemovalBy
Indicates that one entity performs or is responsible for removing snow from another entity or location.
-
E.
snowReliability
Indicates how consistently and dependably snow is present or available in a given context or location.
- 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_69d81c67ba6c819091935650dfb3b895 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de3c8e6d008190af8892f34c5cefbd |
completed | April 14, 2026, 1:09 p.m. |
| PD | Predicate disambiguation | batch_69de05adef888190b023ab42ef5076b6 |
completed | April 14, 2026, 9:15 a.m. |
| PDg | Predicate description generation | batch_69de2398856c81908bed6070e4ca6ab1 |
completed | April 14, 2026, 11:23 a.m. |
Created at: April 9, 2026, 10:20 p.m.