Triple
T17035290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tucker decomposition |
E413306
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | higher-order principal component analysis |
C28168
|
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: higher-order principal component analysis Context triple: [Tucker decomposition, instanceOf, higher-order principal component analysis]
-
A.
third order
A third order is a lay association within a religious tradition whose members live out the spirituality and rule of a first order (such as monks or friars) while remaining in secular life.
-
B.
framework for tensor analysis
chosen
A framework for tensor analysis is a structured system of concepts, operations, and tools that enables the representation, manipulation, and interpretation of multi-dimensional data using tensor algebra and related computational methods.
-
C.
unsupervised learning method
An unsupervised learning method is a type of machine learning approach that discovers patterns, structures, or groupings in unlabeled data without predefined output targets.
-
D.
partition-based clustering method
A partition-based clustering method is an approach that divides a dataset into a predefined number of non-overlapping groups (clusters) by directly assigning each data point to exactly one cluster based on a chosen similarity or distance measure.
-
E.
Hessian general
A Hessian general is a high-ranking military officer from the German state of Hesse, historically known for commanding Hessian troops often employed as auxiliaries in foreign armies, such as those fighting for Britain during the American Revolutionary War.
- 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_69d886cd18288190b006abab23f811b7 |
completed | April 10, 2026, 5:12 a.m. |
Created at: April 10, 2026, 5:33 a.m.