Triple
T32861597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | default mode network |
E840532
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | large-scale brain network |
C12714
|
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: large-scale brain network Context triple: [default mode network, instanceOf, large-scale brain network]
-
A.
cognitive neuroscience model
A cognitive neuroscience model is a theoretical or computational framework that explains how brain structures and neural processes give rise to cognitive functions such as perception, memory, decision-making, and language.
-
B.
neuroscience journal
A neuroscience journal is a periodical publication that disseminates peer-reviewed research, reviews, and scholarly articles on the structure, function, development, and disorders of the nervous system.
-
C.
neuroanatomical feature
chosen
A neuroanatomical feature is a distinct structural component of the nervous system, such as a region, pathway, or cellular arrangement, identifiable by its location, morphology, and functional associations.
-
D.
neurosciences institute
A neurosciences institute is a specialized research and clinical organization dedicated to studying the nervous system, brain function, and related disorders to advance understanding, diagnosis, and treatment.
-
E.
large-scale model
A large-scale model is a computational model, often in machine learning or simulation, that operates with vast numbers of parameters or variables to capture complex patterns or behaviors across extensive datasets or systems.
- 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_69f34942465c819099b3fb47f9044f58 |
completed | April 30, 2026, 12:21 p.m. |
Created at: May 1, 2026, 1:17 a.m.