Triple
T18799331
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | xarray |
E459722
|
entity |
| Predicate | inspiredBy |
P9
|
FINISHED |
| Object | pandas |
—
|
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: pandas | Statement: [xarray, inspiredBy, pandas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: pandas Context triple: [xarray, inspiredBy, pandas]
-
A.
pandas
chosen
pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
-
B.
Pandas Developers
Pandas Developers are the community of programmers and contributors who maintain and advance the pandas Python library for data analysis and manipulation.
-
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.
Pythion
Pythion was an ancient city of Perrhaebia in northern Thessaly, Greece, likely known for its regional religious and strategic significance.
-
E.
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.
- 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_69d8d398c7d4819091cb2f7e48948aeb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a02273b481909bc250144a0ace32 |
completed | April 20, 2026, 3:40 a.m. |
Created at: April 10, 2026, 11:53 a.m.