Triple
T18799345
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | xarray |
E459722
|
entity |
| Predicate | supportsBackend |
P15794
|
FINISHED |
| Object | Pydap |
—
|
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: Pydap | Statement: [xarray, supportsBackend, Pydap]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pydap Context triple: [xarray, supportsBackend, Pydap]
-
A.
OPeNDAP
chosen
OPeNDAP is a data access protocol and software framework that enables remote, web-based access and subsetting of scientific datasets, commonly used in Earth science and climate data systems.
-
B.
NetCDF
NetCDF is a widely used, self-describing, machine-independent data format and set of software libraries designed for storing and sharing array-oriented scientific data, especially in the geosciences.
-
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.
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.
-
E.
PyTables
PyTables is a Python library that provides efficient management, querying, and storage of large amounts of data using the HDF5 format.
- 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.