Triple
T949459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Worcester, Massachusetts |
E20486
|
entity |
| Predicate | averageWinterWeather |
P10789
|
FINISHED |
| Object | cold and snowy winters |
—
|
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: cold and snowy winters | Statement: [Worcester, Massachusetts, averageWinterWeather, cold and snowy winters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averageWinterWeather Context triple: [Worcester, Massachusetts, averageWinterWeather, cold and snowy winters]
-
A.
averageWinterLowTemperature
Indicates the typical minimum temperature experienced during the winter season for a given location or period.
-
B.
wintersIn
Indicates that an entity spends the winter season in a particular place or region.
-
C.
winterCharacteristic
chosen
Indicates a characteristic, feature, or quality that is specifically associated with or typical of winter.
-
D.
averageTemperature
Indicates the typical or mean temperature value associated with an entity over a specified period or context.
-
E.
typicalTemperature
Indicates the usual or characteristic temperature associated with an entity under normal conditions.
- 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_69a493b0f2fc81908cd227480a5356a1 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3c191ac819099ebf3cb32f096d8 |
completed | March 1, 2026, 9:46 p.m. |
| PD | Predicate disambiguation | batch_69a4b29f05f481908814bd11f235e9d0 |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.