Triple
T20367841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Antal Bejczy Center for Intelligent Robotics |
E496961
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | intelligent systems research center |
C43962
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: intelligent systems research center Context triple: [Antal Bejczy Center for Intelligent Robotics, instanceOf, intelligent systems research center]
-
A.
computer science research center
A computer science research center is an institution dedicated to advancing knowledge and innovation in computing through focused research, collaboration, and dissemination of results across areas such as algorithms, systems, artificial intelligence, and human-computer interaction.
-
B.
cognitive science research center
A cognitive science research center is an interdisciplinary institution that investigates the nature of mind, intelligence, and cognition through collaborative studies in psychology, neuroscience, computer science, linguistics, philosophy, and related fields.
-
C.
machine learning research institute
A machine learning research institute is an organization dedicated to advancing the theory, algorithms, and applications of machine learning through systematic research, experimentation, and collaboration.
-
D.
human–computer interaction research center
A human–computer interaction research center is an interdisciplinary organization that studies, designs, and evaluates interactive technologies to improve how people use and experience computer systems.
-
E.
computer vision research laboratory
A computer vision research laboratory is a specialized facility where researchers develop, test, and evaluate algorithms and systems that enable machines to interpret and understand visual information from the world.
- F. None of above. chosen
Provenance (1 batch)
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_69e0b4a4f9b081908a5a021919c21ccb |
completed | April 16, 2026, 10:06 a.m. |
Created at: April 16, 2026, 11:26 a.m.