Triple
T18017530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Snowpipe |
E431031
|
entity |
| Predicate | monitoredBy |
P752
|
FINISHED |
| Object | Snowflake ACCOUNT_USAGE views |
—
|
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 ACCOUNT_USAGE views | Statement: [Snowpipe, monitoredBy, Snowflake ACCOUNT_USAGE views]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Snowflake ACCOUNT_USAGE views Context triple: [Snowpipe, monitoredBy, Snowflake ACCOUNT_USAGE views]
-
A.
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.
-
B.
Snowflake Native Apps
Snowflake Native Apps are applications built and deployed directly within the Snowflake Data Cloud, allowing developers to create, distribute, and monetize data-intensive solutions that run securely where the data lives.
-
C.
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.
-
D.
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.
-
E.
Amazon QuickSight
Amazon QuickSight is a cloud-based business intelligence and data visualization service from AWS that enables users to create interactive dashboards and insights from various data sources.
- 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 ACCOUNT_USAGE views Target entity description: Snowflake ACCOUNT_USAGE views are system-defined views that provide detailed metadata and monitoring information about Snowflake objects and activities, enabling auditing, cost analysis, and operational visibility.
-
A.
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.
-
B.
Snowflake Native Apps
Snowflake Native Apps are applications built and deployed directly within the Snowflake Data Cloud, allowing developers to create, distribute, and monetize data-intensive solutions that run securely where the data lives.
-
C.
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.
-
D.
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.
-
E.
Amazon QuickSight
Amazon QuickSight is a cloud-based business intelligence and data visualization service from AWS that enables users to create interactive dashboards and insights from various data sources.
- F. None of above. chosen
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.