Triple
T4599952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Python scientific stack |
E100298
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | Dask |
E426661
|
NE FINISHED |
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 | Statement: [Python scientific stack, hasComponent, Dask]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dask Context triple: [Python scientific stack, hasComponent, Dask]
-
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.
NVIDIA RAPIDS
NVIDIA RAPIDS is an open-source suite of GPU-accelerated data science and analytics libraries designed to speed up end-to-end machine learning and data processing workflows.
-
C.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
-
D.
Databricks
Databricks is a cloud-based data and AI company best known for its unified analytics platform built around Apache Spark, enabling large-scale data engineering, data science, and machine learning workloads.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69bd43cbc014819098b45f435908f88a |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5971f448819090f6e76c7d3ffc2d |
completed | March 20, 2026, 2:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfa54bb0c819081265a6d159ad790 |
completed | March 21, 2026, 1:54 a.m. |
Created at: March 20, 2026, 1:11 p.m.