Triple
T33589058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biogeochemical Argo |
E860367
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | autonomous profiling float network |
C9443
|
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: autonomous profiling float network Context triple: [Biogeochemical Argo, instanceOf, autonomous profiling float network]
-
A.
plate margin network
A plate margin network is the interconnected system of tectonic plate boundaries and their associated geological structures and processes that collectively govern the distribution and interaction of Earth’s lithospheric plates.
-
B.
deep learning framework
A deep learning framework is a software library or platform that provides tools, abstractions, and optimized components to design, train, and deploy neural network models efficiently.
-
C.
profiling float
chosen
A profiling float is a drifting oceanographic instrument that periodically changes its buoyancy to move vertically through the water column, measuring properties like temperature, salinity, and pressure to create depth profiles over time.
-
D.
normalizing flow model
A normalizing flow model is a generative model that transforms a simple base distribution into a complex target distribution through a sequence of invertible, differentiable mappings with tractable Jacobian determinants.
-
E.
neural network design method
A neural network design method is a systematic approach for selecting, structuring, and configuring neural network architectures and training procedures to solve specific computational or learning tasks.
- 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_69f3497e70e48190951c94d072879bec |
completed | April 30, 2026, 12:22 p.m. |
Created at: May 1, 2026, 1:40 a.m.