Triple
T17520153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dask |
E426661
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | Dask Array |
—
|
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 Array | Statement: [Dask, hasComponent, Dask Array]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dask Array Context triple: [Dask, hasComponent, Dask Array]
-
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.
xarray
xarray is an open-source Python library that provides labeled, N-dimensional arrays and datasets for more intuitive and efficient analysis of multi-dimensional scientific data.
-
D.
NumPy
NumPy is a fundamental Python library that provides efficient multi-dimensional arrays and numerical computing tools widely used in scientific computing and data analysis.
-
E.
RDD
RDD is the three-letter IATA airport code for Redding Municipal Airport in Redding, California.
- 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.