Triple
T23530530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gad Valley |
E576554
|
entity |
| Predicate | averageSnowfallCharacteristic |
P10513
|
FINISHED |
| Object | high 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: high annual snowfall | Statement: [Gad Valley, averageSnowfallCharacteristic, high annual snowfall]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averageSnowfallCharacteristic Context triple: [Gad Valley, averageSnowfallCharacteristic, high 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.
snowAccumulation
Indicates that snow has collected or built up on a surface or in a location over time.
-
D.
hasSnowfallFrequency
Indicates how often snowfall occurs for or at a given entity.
-
E.
snowQuality
Indicates the condition or characteristics of the snow, such as its texture, depth, or suitability for a particular use.
- 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_69e245f5a8848190a2ba42e271c6c31f |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1ac7759e88190aea55c65e24c081f |
completed | April 29, 2026, 7 a.m. |
| PD | Predicate disambiguation | batch_69f1189d75b48190a1c01928a993c9fb |
completed | April 28, 2026, 8:29 p.m. |
Created at: April 17, 2026, 6:09 p.m.