Triple
T17521883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Posit PBC |
E426694
|
entity |
| Predicate | product |
P490
|
FINISHED |
| Object | RStudio Server |
—
|
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: RStudio Server | Statement: [Posit PBC, product, RStudio Server]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: RStudio Server Context triple: [Posit PBC, product, RStudio Server]
-
A.
RStudio
chosen
RStudio is an integrated development environment (IDE) for the R programming language, widely used for data analysis, visualization, and statistical computing.
-
B.
Streamlit Community Cloud
Streamlit Community Cloud is a hosted platform that lets users easily deploy, share, and manage Streamlit data apps directly from their code repositories.
-
C.
SageMaker Studio
SageMaker Studio is Amazon SageMaker’s web-based integrated development environment (IDE) for building, training, and deploying machine learning models at scale.
-
D.
JetBrains Hub
JetBrains Hub is a centralized user management and authentication service by JetBrains that provides single sign-on and access control across multiple JetBrains tools and services.
-
E.
Jupyter Server
Jupyter Server is the backend application that manages and serves Jupyter notebooks, kernels, and related services for frontends like JupyterLab.
- 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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452d2f79881909556894728e255ab |
completed | April 19, 2026, 3:58 a.m. |
Created at: April 10, 2026, 5:49 a.m.