Triple
T18184233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | tidyverse |
E435367
|
entity |
| Predicate | corePackage |
P86880
|
FINISHED |
| Object | tidyr |
—
|
NE NERFINISHED |
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: tidyr | Statement: [tidyverse, corePackage, tidyr]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: tidyr Context triple: [tidyverse, corePackage, tidyr]
-
A.
tidyr
chosen
tidyr is an R package that provides tools for reshaping and organizing data into tidy formats to facilitate analysis and visualization.
-
B.
dplyr
dplyr is a popular R package that provides a consistent, fast, and intuitive grammar of data manipulation for data frames and tibbles.
-
C.
tidyverse
tidyverse is a collection of R packages designed for data science, emphasizing a consistent, human-readable grammar for data manipulation, visualization, and analysis.
-
D.
pandas
pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
-
E.
TABLEAUX
TABLEAUX is an international conference series focused on automated reasoning with analytic tableaux and related proof systems.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b90c7ec081909b4694ccecb449c6 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4dffd0abc81908cc07d28bdc3d48f |
completed | April 19, 2026, 2 p.m. |
Created at: April 10, 2026, 10:31 a.m.