Triple
T18016920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Snowpark DataFrame API |
E431017
|
entity |
| Predicate | marketedAs |
P1395
|
FINISHED |
| Object | Snowpark DataFrame API |
—
|
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 DataFrame API | Statement: [Snowpark DataFrame API, marketedAs, Snowpark DataFrame API]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Snowpark DataFrame API Context triple: [Snowpark DataFrame API, marketedAs, Snowpark DataFrame API]
-
A.
Snowpark DataFrame API
chosen
The Snowpark DataFrame API is a developer framework for building and executing scalable, DataFrame-style data transformations and applications directly within the Snowflake data platform.
-
B.
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.
-
C.
Python (via Snowpark)
Python (via Snowpark) is Snowflake’s integration of the Python language for building and running data pipelines, machine learning, and other data applications directly within the Snowflake data platform.
-
D.
pandas
pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
-
E.
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.
- 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.