Triple
T4371835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | R |
E98913
|
entity |
| Predicate | hasPackage |
P14571
|
FINISHED |
| Object |
ggplot2
ggplot2 is a widely used R package for creating elegant, layered, and highly customizable data visualizations based on the Grammar of Graphics.
|
E436330
|
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: ggplot2 | Statement: [R, hasPackage, ggplot2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ggplot2 Context triple: [R, hasPackage, ggplot2]
-
A.
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.
-
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.
pgfplots
pgfplots is a LaTeX package for creating high-quality plots and graphs based on the PGF/TikZ drawing framework.
-
D.
PowerChart
PowerChart is Cerner's flagship electronic health record (EHR) solution used by healthcare providers to document, manage, and access patient clinical information.
-
E.
Agg
Agg is a high-quality, anti-aliased 2D graphics rendering engine commonly used as a backend in plotting and visualization libraries.
- 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: ggplot2 Triple: [R, hasPackage, ggplot2]
Generated description
ggplot2 is a widely used R package for creating elegant, layered, and highly customizable data visualizations based on the Grammar of Graphics.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ggplot2 Target entity description: ggplot2 is a widely used R package for creating elegant, layered, and highly customizable data visualizations based on the Grammar of Graphics.
-
A.
tidyverse
tidyverse is a collection of R packages designed for data science, emphasizing a consistent, human-readable grammar for data manipulation, visualization, and analysis.
-
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.
Plotly
Plotly is an interactive, open-source graphing and data visualization library widely used in Python for creating rich, web-based charts and dashboards.
-
D.
pgfplots
pgfplots is a LaTeX package for creating high-quality plots and graphs based on the PGF/TikZ drawing framework.
-
E.
PowerChart
PowerChart is Cerner's flagship electronic health record (EHR) solution used by healthcare providers to document, manage, and access patient clinical information.
- 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_69b3454db3708190aeafd814413c4c3d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3521dffbc8190b9300a7f4f64bdc0 |
completed | March 12, 2026, 11:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5e50bcc9481909b0b9d60198dce63 |
completed | March 14, 2026, 10:45 p.m. |
| NEDg | Description generation | batch_69b5eeadd68881909820a75aaff9d8d5 |
completed | March 14, 2026, 11:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5ef36f2bc8190a21e0f2fadbdd697 |
completed | March 14, 2026, 11:28 p.m. |
Created at: March 12, 2026, 11:17 p.m.