Triple
T4326235
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | sns |
E96639
|
entity |
| Predicate | providesAccessTo |
P1985
|
FINISHED |
| Object | seaborn.violinplot |
E17844
|
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: seaborn.violinplot | Statement: [sns, providesAccessTo, seaborn.violinplot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: seaborn.violinplot Context triple: [sns, providesAccessTo, seaborn.violinplot]
-
A.
Seaborn
chosen
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.
-
B.
Plotly
Plotly is an interactive, open-source graphing and data visualization library widely used in Python for creating rich, web-based charts and dashboards.
-
C.
Tukey's biweight
Tukey's biweight is a robust statistical estimator that downweights outliers to provide resistant measures of central tendency or regression fits.
-
D.
Tukey's fences
Tukey's fences are a statistical rule-of-thumb method for identifying outliers in a data set using interquartile range–based cutoff points.
-
E.
Matplotlib
Matplotlib is a widely used Python plotting library for creating static, animated, and interactive visualizations.
- 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_69b34542fd908190b11b08faad8decfd |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3513020f481909ff2fec3934f3002 |
completed | March 12, 2026, 11:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5d09861a4819086a88bb42a8ea2e4 |
completed | March 14, 2026, 9:18 p.m. |
Created at: March 12, 2026, 11:13 p.m.