Triple
T32861599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | default mode network |
E840532
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | resting-state network |
C61007
|
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: resting-state network Context triple: [default mode network, instanceOf, resting-state network]
-
A.
recurrent artificial neural network
A recurrent artificial neural network is a type of neural network where connections form directed cycles, allowing information to persist over time and enabling the modeling of sequential or temporal data.
-
B.
fictional neural interface system
A fictional neural interface system is an imagined technology that directly links the human brain with computers or networks to enable seamless communication, control, and data exchange through thought alone.
-
C.
retreat network
A retreat network is an interconnected system of venues, facilitators, and resources that collaboratively organize, host, and support retreats across multiple locations and communities.
-
D.
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.
-
E.
state-controlled network of organizations
A state-controlled network of organizations is a coordinated system of entities whose structures, activities, and decision-making are centrally directed or heavily influenced by government authorities to achieve political, economic, or social objectives.
- 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_69f34942465c819099b3fb47f9044f58 |
completed | April 30, 2026, 12:21 p.m. |
Created at: May 1, 2026, 1:17 a.m.