Triple
T22503654
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BIOVIA |
E556335
|
entity |
| Predicate | hasProduct |
P3585
|
FINISHED |
| Object | BIOVIA Notebook |
—
|
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: BIOVIA Notebook | Statement: [BIOVIA, hasProduct, BIOVIA Notebook]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BIOVIA Notebook Context triple: [BIOVIA, hasProduct, BIOVIA Notebook]
-
A.
BIOVIA
chosen
BIOVIA is a Dassault Systèmes software brand focused on scientific informatics and molecular modeling solutions for life sciences, materials science, and chemistry.
-
B.
Neptune Workbench
Neptune Workbench is an integrated, Jupyter-based development environment for building, querying, and visualizing graph applications on Amazon Neptune.
-
C.
DNAnexus
DNAnexus is a cloud-based genomics and bioinformatics platform that enables large-scale DNA data analysis, management, and collaboration for research and clinical applications.
-
D.
Citavi
Citavi is a reference management and knowledge organization software used by researchers and students to collect, manage, and cite sources in academic writing.
-
E.
EMR Notebooks
EMR Notebooks are managed Jupyter notebook environments in Amazon EMR that let users interactively develop, visualize, and run big data applications on EMR clusters.
- 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_69e11e555edc81909ca803587dafd747 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15d5a01888190ba65a05616b63cbe |
completed | April 29, 2026, 1:22 a.m. |
Created at: April 16, 2026, 8:50 p.m.