dplyr
E436331
dplyr is a popular R package that provides a consistent, fast, and intuitive grammar of data manipulation for data frames and tibbles.
All labels observed (1)
| Label | Occurrences |
|---|---|
| dplyr canonical | 4 |
How this entity was disambiguated
This entity first appeared as the object of triple T4371836 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: dplyr Context triple: [R, hasPackage, dplyr]
-
A.
pandas
pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
-
B.
PDL
PDL is a former name for USL League Two, a North American pre-professional soccer league that serves as a key development platform for college-aged and aspiring professional players.
-
C.
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.
-
D.
daa
daa is the Irish state-owned airport operator responsible for managing Dublin Airport and other aviation and travel-related businesses.
-
E.
Tabularium
The Tabularium was the official records office of ancient Rome, a monumental state archive building overlooking the Roman Forum.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: dplyr Target entity description: dplyr is a popular R package that provides a consistent, fast, and intuitive grammar of data manipulation for data frames and tibbles.
-
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.
pandas
pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
-
C.
PDL
PDL is a former name for USL League Two, a North American pre-professional soccer league that serves as a key development platform for college-aged and aspiring professional players.
-
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.
daa
daa is the Irish state-owned airport operator responsible for managing Dublin Airport and other aviation and travel-related businesses.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
R package
ⓘ
software library ⓘ |
| compatibleWith |
base R pipe operator
ⓘ
magrittr pipe operator ⓘ |
| creator | Hadley Wickham ⓘ |
| designGoal |
consistency
ⓘ
intuitive syntax ⓘ speed ⓘ |
| developedBy |
RStudio (now Posit)
ⓘ
linked to:
RStudio
|
| documentationUrl | https://dplyr.tidyverse.org ⓘ |
| focusesOn |
data manipulation
ⓘ
data transformation ⓘ data wrangling ⓘ |
| hostedOn |
CRAN
ⓘ
GitHub ⓘ |
| implements | grammar of data manipulation ⓘ |
| keyword |
data analysis
ⓘ
data manipulation ⓘ tidy data ⓘ |
| license | MIT License ⓘ |
| maintainer |
Hadley Wickham
ⓘ
Posit PBC ⓘ |
| partOfEcosystem | tidyverse ⓘ |
| programmingLanguage | R ⓘ |
| providesFunction |
arrange
ⓘ
bind_cols ⓘ bind_rows ⓘ distinct ⓘ filter ⓘ group_by ⓘ join functions ⓘ mutate ⓘ relocate ⓘ rename ⓘ select ⓘ slice ⓘ summarise ⓘ summarize ⓘ ungroup ⓘ |
| sourceRepository | https://github.com/tidyverse/dplyr ⓘ |
| supports | piping workflows ⓘ |
| supportsDataStructure |
data frame
ⓘ
tibble ⓘ |
| usedFor |
data preprocessing
ⓘ
exploratory data analysis ⓘ |
| usesLanguage | C++ ⓘ |
| usesLibrary |
Rcpp
ⓘ
magrittr ⓘ rlang ⓘ tibble ⓘ |
| writtenIn | R ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: dplyr Description of subject: dplyr is a popular R package that provides a consistent, fast, and intuitive grammar of data manipulation for data frames and tibbles.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.