Triple
T4240209
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rusutsu |
E95392
|
entity |
| Predicate | typicalSnowfall |
P10513
|
FINISHED |
| Object | heavy annual snowfall |
—
|
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: heavy annual snowfall | Statement: [Rusutsu, typicalSnowfall, heavy annual snowfall]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSnowfall Context triple: [Rusutsu, typicalSnowfall, heavy annual snowfall]
-
A.
averageAnnualSnowfall
chosen
Indicates the typical amount of snow that falls in a given location over the course of a year, averaged across multiple years.
-
B.
hasSnowfall
Indicates that a location or area experiences or contains snowfall.
-
C.
snowfallRecord
Indicates that a specific amount of snow has been measured or documented for a particular place and time.
-
D.
typicalIceThickness
Indicates the usual or characteristic thickness of ice under normal or representative conditions.
-
E.
hasSnowOccasionally
Indicates that the subject experiences snowfall at irregular or infrequent intervals rather than regularly or never.
- 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_69b3453d91548190b4d4ef8fe52aa2ac |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34e77fb5c8190b298818acb68ff63 |
completed | March 12, 2026, 11:38 p.m. |
| PD | Predicate disambiguation | batch_69b347f587148190a1830503459939b6 |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:05 p.m.