Triple
T18017531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Snowpipe |
E431031
|
entity |
| Predicate | monitoredBy |
P752
|
FINISHED |
| Object | Snowflake INFORMATION_SCHEMA 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 INFORMATION_SCHEMA views | Statement: [Snowpipe, monitoredBy, Snowflake INFORMATION_SCHEMA views]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Snowflake INFORMATION_SCHEMA views Context triple: [Snowpipe, monitoredBy, Snowflake INFORMATION_SCHEMA 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.
Dynamic Management Views (DMVs)
Dynamic Management Views (DMVs) are specialized system views in SQL Server that expose real-time internal state and performance data to help monitor, diagnose, and tune the database engine and its features.
- 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 INFORMATION_SCHEMA views Target entity description: Snowflake INFORMATION_SCHEMA views are system-defined metadata views that provide detailed information about database objects, operations, and usage within a Snowflake account.
-
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.
Dynamic Management Views (DMVs)
Dynamic Management Views (DMVs) are specialized system views in SQL Server that expose real-time internal state and performance data to help monitor, diagnose, and tune the database engine and its features.
- 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.