Triple
T17587567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Snowpark for JavaScript |
E428363
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | Snowflake SQL engine |
—
|
NE NERFINISHED |
How this triple was built (3 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: Snowflake SQL engine | Statement: [Snowpark for JavaScript, integratesWith, Snowflake SQL engine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Snowflake SQL engine Context triple: [Snowpark for JavaScript, integratesWith, Snowflake SQL engine]
-
A.
Snowflake Data Cloud
Snowflake Data Cloud is a cloud-native data platform that enables organizations to store, integrate, and analyze data at scale across multiple clouds with a unified, fully managed service.
-
B.
Snowflake virtual warehouses
Snowflake virtual warehouses are scalable compute clusters in the Snowflake cloud data platform that execute queries and data processing workloads independently of storage.
-
C.
BlazingSQL
BlazingSQL is an open-source SQL engine that enables GPU-accelerated data processing and analytics, often used within the NVIDIA RAPIDS ecosystem for high-performance query execution on large datasets.
-
D.
Apache Iceberg
Apache Iceberg is an open table format for huge analytic datasets that enables reliable, high-performance querying and data management in data lake environments.
-
E.
Scala (via Snowpark)
Scala (via Snowpark) is a way to use the Scala programming language within Snowflake’s Snowpark developer framework to build and run data pipelines, transformations, and applications directly in the Snowflake Data Cloud.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Snowflake SQL engine Target entity description: Snowflake SQL engine is the cloud-based, massively parallel processing core of the Snowflake data platform that executes SQL queries and powers data warehousing, analytics, and data engineering workloads.
-
A.
Snowflake Data Cloud
Snowflake Data Cloud is a cloud-native data platform that enables organizations to store, integrate, and analyze data at scale across multiple clouds with a unified, fully managed service.
-
B.
Snowflake virtual warehouses
chosen
Snowflake virtual warehouses are scalable compute clusters in the Snowflake cloud data platform that execute queries and data processing workloads independently of storage.
-
C.
BlazingSQL
BlazingSQL is an open-source SQL engine that enables GPU-accelerated data processing and analytics, often used within the NVIDIA RAPIDS ecosystem for high-performance query execution on large datasets.
-
D.
Apache Iceberg
Apache Iceberg is an open table format for huge analytic datasets that enables reliable, high-performance querying and data management in data lake environments.
-
E.
Scala (via Snowpark)
Scala (via Snowpark) is a way to use the Scala programming language within Snowflake’s Snowpark developer framework to build and run data pipelines, transformations, and applications directly in the Snowflake Data Cloud.
- F. None of above.
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_69d889e1030481909950e140c63255b9 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e469e41bf08190963848f1597b6e9f |
completed | April 19, 2026, 5:36 a.m. |
Created at: April 10, 2026, 5:51 a.m.