Triple
T3221090
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlotte Pass |
E67511
|
entity |
| Predicate | snowReliability |
P46612
|
FINISHED |
| Object | one of the most reliable in Australia |
—
|
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: one of the most reliable in Australia | Statement: [Charlotte Pass, snowReliability, one of the most reliable in Australia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: snowReliability Context triple: [Charlotte Pass, snowReliability, one of the most reliable in Australia]
-
A.
snowRemovalBy
Indicates that one entity performs or is responsible for removing snow from another entity or location.
-
B.
snowCover
Indicates that one entity is covered by or blanketed with snow.
-
C.
hasSnowfall
Indicates that a location or area experiences or contains snowfall.
-
D.
winterStatus
Indicates the condition, phase, or circumstances associated with the winter season for a given entity or context.
-
E.
snowfallRecord
Indicates that a specific amount of snow has been measured or documented for a particular place and time.
- 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_69ad858b8adc8190ad989712c87a476b |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adae16f20081909d7f3bac016f961d |
completed | March 8, 2026, 5:12 p.m. |
| PD | Predicate disambiguation | batch_69ad9e0bb6c48190a0659c67d40ee37c |
completed | March 8, 2026, 4:04 p.m. |
| PDg | Predicate description generation | batch_69ada148e9108190b363dd0f1a94ac8e |
completed | March 8, 2026, 4:18 p.m. |
Created at: March 8, 2026, 3:08 p.m.