Triple
T17520155
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dask |
E426661
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object |
Dask Bag
Dask Bag is a high-level Dask collection for parallel processing of large, semi-structured or unstructured datasets, similar to a distributed Python list.
|
E426661
|
NE FINISHED |
How this triple was built (4 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 Bag | Statement: [Dask, hasComponent, Dask Bag]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dask Bag Context triple: [Dask, hasComponent, Dask Bag]
-
A.
Dask
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.
RDD
RDD is the three-letter IATA airport code for Redding Municipal Airport in Redding, California.
-
D.
Apache Beam
Apache Beam is an open-source unified programming model for defining and executing batch and streaming data processing pipelines across multiple execution engines.
-
E.
Apache Iceberg
Apache Iceberg is an open table format for huge analytic datasets that enables reliable, high-performance querying and data management in data lake environments.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Dask Bag Triple: [Dask, hasComponent, Dask Bag]
Generated description
Dask Bag is a high-level Dask collection for parallel processing of large, semi-structured or unstructured datasets, similar to a distributed Python list.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dask Bag Target entity description: Dask Bag is a high-level Dask collection for parallel processing of large, semi-structured or unstructured datasets, similar to a distributed Python list.
-
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.
RDD
RDD is the three-letter IATA airport code for Redding Municipal Airport in Redding, California.
-
D.
Apache Beam
Apache Beam is an open-source unified programming model for defining and executing batch and streaming data processing pipelines across multiple execution engines.
-
E.
Apache Iceberg
Apache Iceberg is an open table format for huge analytic datasets that enables reliable, high-performance querying and data management in data lake environments.
- F. None of above.
Provenance (5 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. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01c94237d08190bb1f874735c87803 |
completed | May 11, 2026, 12:19 p.m. |
| NEDg | Description generation | batch_6a01cabea2b48190a690b17a88d45b40 |
completed | May 11, 2026, 12:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01cefa08f8819086cb86ce22193baa |
completed | May 11, 2026, 12:43 p.m. |
Created at: April 10, 2026, 5:49 a.m.