Triple
T18015958
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NumFOCUS |
E430998
|
entity |
| Predicate | supportsProject |
P12986
|
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: [NumFOCUS, supportsProject, Pandas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pandas Context triple: [NumFOCUS, supportsProject, 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.
Pythion
Pythion was an ancient city of Perrhaebia in northern Thessaly, Greece, likely known for its regional religious and strategic significance.
-
C.
Pandas Developers
Pandas Developers are the community of programmers and contributors who maintain and advance the pandas Python library for data analysis and manipulation.
-
D.
Seaborn
Seaborn is a Python data visualization library built on top of Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics.
-
E.
Seaborn
Seaborn is a masculine given name of English origin, historically used in colonial America and associated with individuals such as Seaborn Cotton.
- 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b523f588819097389e067dda7f23 |
completed | April 19, 2026, 10:57 a.m. |
Created at: April 10, 2026, 10:24 a.m.