Triple
T4371836
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | R |
E98913
|
entity |
| Predicate | hasPackage |
P14571
|
FINISHED |
| Object |
dplyr
dplyr is a popular R package that provides a consistent, fast, and intuitive grammar of data manipulation for data frames and tibbles.
|
E436331
|
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: dplyr | Statement: [R, hasPackage, dplyr]
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.
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: dplyr Triple: [R, hasPackage, dplyr]
Generated description
dplyr is a popular R package that provides a consistent, fast, and intuitive grammar of data manipulation for data frames and tibbles.
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
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.