Triple
T18016879
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Snowpark DataFrame API |
E431017
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Snowpark |
—
|
NE NERFINISHED |
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: Snowpark | Statement: [Snowpark DataFrame API, partOf, Snowpark]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Snowpark Context triple: [Snowpark DataFrame API, partOf, Snowpark]
-
A.
Snowpark
chosen
Snowpark is a developer framework from Snowflake that lets data engineers and data scientists write data pipelines and applications in languages like Python, Java, and Scala directly within the Snowflake data platform.
-
B.
Story Park
Story Park is a public neighborhood park located in Alhambra, California, offering recreational and community facilities for local residents.
-
C.
Ski Station
Ski Station is a railway station in Ski, Norway, serving as a key transport hub for local and regional train services.
-
D.
Phoenix Snow Park
Phoenix Snow Park is a South Korean winter sports venue known for hosting freestyle skiing and snowboarding events, including competitions at the 2018 Pyeongchang Winter Olympics.
-
E.
Ice Park
Ice Park is a themed attraction within Dubai's Zabeel Park that features ice- and winter-inspired displays and activities in an otherwise warm, urban setting.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b9be5d0c819097e006f32d98753a |
completed | April 19, 2026, 11:17 a.m. |
Created at: April 10, 2026, 10:24 a.m.