Triple
T17520154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dask |
E426661
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | Dask DataFrame |
—
|
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: Dask DataFrame | Statement: [Dask, hasComponent, Dask DataFrame]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dask DataFrame Context triple: [Dask, hasComponent, Dask DataFrame]
-
A.
Dask
chosen
Dask is an open-source parallel computing library for Python that enables scalable, distributed data processing and analytics using familiar interfaces like NumPy, pandas, and scikit-learn.
-
B.
Dask-cuDF
Dask-cuDF is a RAPIDS library that enables distributed, GPU-accelerated DataFrame processing by integrating cuDF with Dask for scalable data analytics.
-
C.
Snowpark DataFrame API
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.
-
D.
cuDF
cuDF is a GPU-accelerated DataFrame library from NVIDIA’s RAPIDS ecosystem that enables fast, pandas-like data manipulation and analytics on large datasets.
-
E.
PySpark
PySpark is the Python API for Apache Spark, enabling large-scale data processing, analysis, and machine learning using Python.
- 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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452d23cf08190925510344fa36f57 |
completed | April 19, 2026, 3:58 a.m. |
Created at: April 10, 2026, 5:49 a.m.