Triple
T816372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NumPy |
E17659
|
entity |
| Predicate | influenced |
P9
|
FINISHED |
| Object | pandas |
E17660
|
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: pandas | Statement: [NumPy, influenced, pandas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: pandas Context triple: [NumPy, influenced, 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.
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.
-
C.
Power Pivot
Power Pivot is an Excel data modeling and analysis add-in that enables users to create sophisticated data models, relationships, and DAX calculations for business intelligence reporting.
-
D.
Power Query
Power Query is a data connection and transformation tool used to import, clean, and reshape data from various sources before analysis in Microsoft Power BI and other Microsoft products.
-
E.
NumPy
NumPy is a fundamental Python library that provides efficient multi-dimensional arrays and numerical computing tools widely used in scientific computing and data analysis.
- 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_69a4937bcaac8190a322524ac6f45a5a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ab621d2c819083f10bff4f66c482 |
completed | March 1, 2026, 9:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a76d8d1a448190be8494fa2776615a |
completed | March 3, 2026, 11:23 p.m. |
Created at: March 1, 2026, 7:38 p.m.