Triple
T28290476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paradise area of Mount Rainier National Park |
E713410
|
entity |
| Predicate | hasSnowfallRecord |
P3555
|
FINISHED |
| Object | among highest measured annual snowfalls in the United States |
—
|
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: among highest measured annual snowfalls in the United States | Statement: [Paradise area of Mount Rainier National Park, hasSnowfallRecord, among highest measured annual snowfalls in the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSnowfallRecord Context triple: [Paradise area of Mount Rainier National Park, hasSnowfallRecord, among highest measured annual snowfalls in the United States]
-
A.
snowfallRecord
chosen
Indicates that a specific amount of snow has been measured or documented for a particular place and time.
-
B.
hasSnowfall
Indicates that a location or area experiences or contains snowfall.
-
C.
hasSnowfallFrequency
Indicates how often snowfall occurs for or at a given entity.
-
D.
hasHeavySnowfall
Indicates that a location or area is experiencing or characterized by a large amount of snowfall.
-
E.
hasSnowAccumulationRate
Indicates the rate at which snow is accumulating on a surface or in a specified area over time.
- 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_69efb52371d88190a1381c4e58a3b731 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f644839aac8190b57358684d2316b6 |
completed | May 2, 2026, 6:37 p.m. |
| PD | Predicate disambiguation | batch_69f641e0fde08190bf06a1c5b388aa84 |
completed | May 2, 2026, 6:26 p.m. |
Created at: April 27, 2026, 11:29 p.m.