Triple
T15928688
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kunanyi |
E386268
|
entity |
| Predicate | hasSnowfallFrequency |
P120585
|
FINISHED |
| Object | regular winter 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: regular winter snowfall | Statement: [Kunanyi, hasSnowfallFrequency, regular winter snowfall]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSnowfallFrequency Context triple: [Kunanyi, hasSnowfallFrequency, regular winter snowfall]
-
A.
hasSnowfall
Indicates that a location or area experiences or contains snowfall.
-
B.
averageAnnualSnowfall
Indicates the typical amount of snow that falls in a given location over the course of a year, averaged across multiple years.
-
C.
hasSnowfallUnit
Indicates the unit of measurement used to express the amount or depth of snowfall in a given context.
-
D.
hasSnowOccasionally
Indicates that the subject experiences snowfall at irregular or infrequent intervals rather than regularly or never.
-
E.
hasSeasonalSnowCover
Indicates that an entity is covered by snow during certain seasons or periods of the year, rather than permanently.
- 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_69d86da750008190987eb26be3f6c118 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e172b48b308190bc430b2308cbc75b |
completed | April 16, 2026, 11:37 p.m. |
| PD | Predicate disambiguation | batch_69e142cf5c548190a931f7b58144cd31 |
completed | April 16, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69e172b213e481909ee0c05e16229a26 |
completed | April 16, 2026, 11:37 p.m. |
Created at: April 10, 2026, 4:52 a.m.