Triple
T20170393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Snow Town (China Snow Town) |
E491942
|
entity |
| Predicate | averageSnowDepth |
P117020
|
FINISHED |
| Object | often exceeds 2 meters in winter |
—
|
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: often exceeds 2 meters in winter | Statement: [Snow Town (China Snow Town), averageSnowDepth, often exceeds 2 meters in winter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averageSnowDepth Context triple: [Snow Town (China Snow Town), averageSnowDepth, often exceeds 2 meters in winter]
-
A.
averageAnnualSnowfall
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
chosen
Indicates that snow has collected or built up on a surface or in a location over time.
-
D.
snowfallRecord
Indicates that a specific amount of snow has been measured or documented for a particular place and time.
-
E.
hasSnowfallFrequency
Indicates how often snowfall occurs for or at a given entity.
- 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_69da6266c6888190bc1a3ecf24814d34 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e66847ed9481908e6b23b399fa7005 |
completed | April 20, 2026, 5:54 p.m. |
| PD | Predicate disambiguation | batch_69e55b0c11cc8190836d1eee5945f000 |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 11, 2026, 11:35 p.m.