Triple
T21309144
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Myerstown, Pennsylvania |
E525281
|
entity |
| Predicate | averageSnowfall |
P10513
|
FINISHED |
| Object | typical of south-central Pennsylvania |
—
|
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: typical of south-central Pennsylvania | Statement: [Myerstown, Pennsylvania, averageSnowfall, typical of south-central Pennsylvania]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averageSnowfall Context triple: [Myerstown, Pennsylvania, averageSnowfall, typical of south-central Pennsylvania]
-
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.
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_69e0b518b8948190ad69cf9a8784d397 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e75aa916548190a11f8bb4255e3fed |
completed | April 21, 2026, 11:08 a.m. |
| PD | Predicate disambiguation | batch_69e61612ab748190a72b8703b938abcb |
completed | April 20, 2026, 12:03 p.m. |
Created at: April 16, 2026, 4:06 p.m.