Triple
T9631719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berkeley Artificial Intelligence Research Lab |
E232819
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | machine learning research group |
C4221
|
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: machine learning research group Context triple: [Berkeley Artificial Intelligence Research Lab, instanceOf, machine learning research group]
-
A.
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.
-
B.
machine learning researcher
A machine learning researcher is a specialist who develops, analyzes, and improves algorithms and models that enable computers to learn from data and make predictions or decisions.
-
C.
machine learning library
A machine learning library is a collection of tools, algorithms, and interfaces that simplifies building, training, evaluating, and deploying machine learning models.
-
D.
university research team
chosen
A university research team is a collaborative group of faculty, students, and sometimes external partners who systematically investigate specific academic or scientific questions to generate new knowledge and publish their findings.
-
E.
Microsoft research lab
A Microsoft research lab is a specialized facility where scientists and engineers conduct advanced research in computer science and related fields to develop innovative technologies and solutions for Microsoft and the broader tech community.
- F. None of above.
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_69ca848940cc8190b97cec654cb3bb4a |
completed | March 30, 2026, 2:11 p.m. |
Created at: March 30, 2026, 8:11 p.m.