Triple
T18799340
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | xarray |
E459722
|
entity |
| Predicate | supportsBackend |
P15794
|
FINISHED |
| Object |
h5netcdf
h5netcdf is a Python library that provides a NetCDF4-like interface for reading and writing data stored in HDF5 files.
|
E1342434
|
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: h5netcdf | Statement: [xarray, supportsBackend, h5netcdf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: h5netcdf Context triple: [xarray, supportsBackend, h5netcdf]
-
A.
h5py
h5py is a Python library that provides a high-level, NumPy-friendly interface for reading and writing HDF5 files used for storing large numerical datasets.
-
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.
HDF
HDF is the acronym for the Hungarian Defence Forces, the unified military organization responsible for Hungary’s national defense and participation in international security operations.
-
D.
HDF
HDF (Hierarchical Data Format) is a widely used file format and data model designed for storing and organizing large, complex scientific and engineering datasets.
-
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. 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: h5netcdf Triple: [xarray, supportsBackend, h5netcdf]
Generated description
h5netcdf is a Python library that provides a NetCDF4-like interface for reading and writing data stored in HDF5 files.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: h5netcdf Target entity description: h5netcdf is a Python library that provides a NetCDF4-like interface for reading and writing data stored in HDF5 files.
-
A.
h5py
h5py is a Python library that provides a high-level, NumPy-friendly interface for reading and writing HDF5 files used for storing large numerical datasets.
-
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.
HDF
HDF is the acronym for the Hungarian Defence Forces, the unified military organization responsible for Hungary’s national defense and participation in international security operations.
-
D.
HDF
HDF (Hierarchical Data Format) is a widely used file format and data model designed for storing and organizing large, complex scientific and engineering datasets.
-
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. chosen
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_69d8d398c7d4819091cb2f7e48948aeb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a02273b481909bc250144a0ace32 |
completed | April 20, 2026, 3:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a054723def4819097961a04087a54a3 |
completed | May 14, 2026, 3:53 a.m. |
| NEDg | Description generation | batch_6a0549358ba081909eb649898d1b3a8a |
completed | May 14, 2026, 4:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0549c825f481909d2da63688b36186 |
completed | May 14, 2026, 4:04 a.m. |
Created at: April 10, 2026, 11:53 a.m.