Triple
T18134363
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Snowpark for Scala |
E434097
|
entity |
| Predicate | provides |
P490
|
FINISHED |
| Object | 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: DataFrame API | Statement: [Snowpark for Scala, provides, DataFrame API]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DataFrame API Context triple: [Snowpark for Scala, provides, 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.
pandas
pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
-
C.
Table API
Table API is a schema-less, key-value data access interface in Azure Cosmos DB designed for scalable, low-latency storage and retrieval of tabular data.
-
D.
Table API
Table API is Apache Flink’s high-level, declarative interface for expressing data processing and analytics using relational-style operations on streaming and batch data.
-
E.
DataSet API
DataSet API is Apache Flink’s now-legacy batch processing API for defining and executing scalable, distributed data transformations.
- 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_69d8b909e8cc81908df4cc2b8ea6d11f |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4de055c608190a090c2737904e5f9 |
completed | April 19, 2026, 1:52 p.m. |
Created at: April 10, 2026, 10:29 a.m.