Triple
T18184241
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | tidyverse |
E435367
|
entity |
| Predicate | includesPackage |
P49317
|
FINISHED |
| Object |
modelr
modelr is an R package that provides tools for modeling within the tidyverse ecosystem, simplifying the process of building, evaluating, and visualizing statistical models.
|
E1311375
|
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: modelr | Statement: [tidyverse, includesPackage, modelr]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: modelr Context triple: [tidyverse, includesPackage, modelr]
-
A.
GLM
GLM is the National Rail station code for Gillingham railway station in Kent, England.
-
B.
MLR
MLR is a professional rugby union league in North America featuring teams from the United States and Canada.
-
C.
statsmodels
statsmodels is a Python library for statistical modeling and econometrics, providing tools for estimating and interpreting a wide range of statistical models and tests.
-
D.
LGLM
LGLM is the ICAO airport code for Limnos International Airport, serving the island of Lemnos in Greece.
-
E.
Tobit model
The Tobit model is an econometric regression model designed for situations where the dependent variable is censored, allowing consistent estimation when observations are only partially observed beyond certain limits.
- 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: modelr Triple: [tidyverse, includesPackage, modelr]
Generated description
modelr is an R package that provides tools for modeling within the tidyverse ecosystem, simplifying the process of building, evaluating, and visualizing statistical models.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: modelr Target entity description: modelr is an R package that provides tools for modeling within the tidyverse ecosystem, simplifying the process of building, evaluating, and visualizing statistical models.
-
A.
GLM
GLM is the National Rail station code for Gillingham railway station in Kent, England.
-
B.
MLR
MLR is a professional rugby union league in North America featuring teams from the United States and Canada.
-
C.
statsmodels
statsmodels is a Python library for statistical modeling and econometrics, providing tools for estimating and interpreting a wide range of statistical models and tests.
-
D.
LGLM
LGLM is the ICAO airport code for Limnos International Airport, serving the island of Lemnos in Greece.
-
E.
Tobit model
The Tobit model is an econometric regression model designed for situations where the dependent variable is censored, allowing consistent estimation when observations are only partially observed beyond certain limits.
- 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_69d8b90c7ec081909b4694ccecb449c6 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4dffd0abc81908cc07d28bdc3d48f |
completed | April 19, 2026, 2 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0398032e3081909455845718c73b78 |
completed | May 12, 2026, 9:13 p.m. |
| NEDg | Description generation | batch_6a03990a2bec8190b5d6a472bc9bb3fb |
completed | May 12, 2026, 9:18 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0399c98a88819090e579324d9423e5 |
completed | May 12, 2026, 9:21 p.m. |
Created at: April 10, 2026, 10:31 a.m.