Triple
T1057954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Syracuse |
E22838
|
entity |
| Predicate | hasSnowfallCharacteristic |
P10789
|
FINISHED |
| Object | one of the snowiest major U.S. cities |
—
|
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: one of the snowiest major U.S. cities | Statement: [Syracuse, hasSnowfallCharacteristic, one of the snowiest major U.S. cities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSnowfallCharacteristic Context triple: [Syracuse, hasSnowfallCharacteristic, one of the snowiest major U.S. cities]
-
A.
hasSnowAtHighElevations
Indicates that snow is present in areas located at higher elevations within a given region or context.
-
B.
winterCharacteristic
chosen
Indicates a characteristic, feature, or quality that is specifically associated with or typical of winter.
-
C.
snowCover
Indicates that one entity is covered by or blanketed with snow.
-
D.
averageAnnualSnowfall
Indicates the typical amount of snow that falls in a given location over the course of a year, averaged across multiple years.
-
E.
snowfallRecord
Indicates that a specific amount of snow has been measured or documented for a particular place and 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_69a493dada0481909c43649f9843ea91 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4ba6e35ac8190802341c31bda0e3b |
completed | March 1, 2026, 10:15 p.m. |
| PD | Predicate disambiguation | batch_69a4b7340a048190807363f19d17a58f |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.