Triple
T18257187
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Simulink |
E437246
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | MATLAB |
—
|
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: MATLAB | Statement: [Simulink, partOf, MATLAB]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MATLAB Context triple: [Simulink, partOf, MATLAB]
-
A.
MATLAB
chosen
MATLAB is a high-level programming language and interactive environment widely used for numerical computing, data analysis, algorithm development, and visualization, particularly in engineering and scientific research.
-
B.
MATLAB Central
MATLAB Central is an online community hub where MATLAB and Simulink users share code, ask and answer questions, and collaborate on technical computing projects.
-
C.
Octave
Octave is a masculine given name of French origin, historically borne by several notable figures in literature, music, and the arts.
-
D.
MATLAB Toolboxes
MATLAB Toolboxes are specialized add-on collections of functions and apps that extend MATLAB’s capabilities for tasks such as signal processing, machine learning, control systems, and more.
-
E.
Statistics and Machine Learning Toolbox
Statistics and Machine Learning Toolbox is a MATLAB add-on that provides functions and apps for statistical analysis, predictive modeling, and machine learning.
- 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_69d8b913351c8190932b6a426de04b41 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4fd86e21081909c049082949b95c6 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 10:34 a.m.