Triple
T17035306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tucker decomposition |
E413306
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | HOSVD |
E413306
|
NE FINISHED |
How this triple was built (2 steps)
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.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: HOSVD | Statement: [Tucker decomposition, relatedTo, HOSVD]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HOSVD Context triple: [Tucker decomposition, relatedTo, HOSVD]
-
A.
SVD
SVD is the abbreviation for the Special Victims Division, a specialized police unit that investigates sensitive crimes such as sexual offenses and crimes against vulnerable victims.
-
B.
SVD
SVD is the IATA airport code for Argyle International Airport, the main international gateway to Saint Vincent and the Grenadines in the Caribbean.
-
C.
Tucker decomposition in multilinear algebra
chosen
Tucker decomposition in multilinear algebra is a form of higher-order principal component analysis that factorizes a tensor into a core tensor multiplied by factor matrices along each mode, enabling dimensionality reduction and structure discovery in multiway data.
-
D.
Householder transformation
The Householder transformation is a linear algebra technique that uses reflections to orthogonally transform vectors and matrices, commonly employed in QR decomposition and numerical algorithms.
-
E.
Kronecker product
The Kronecker product is a matrix operation that forms a large block matrix from two smaller matrices and is widely used in linear algebra, quantum computing, and signal processing.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
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. |
| NER | Named-entity recognition | batch_69e3d8f05824819091d2aa02e5591e26 |
completed | April 18, 2026, 7:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a011b59ad3c8190914ee6f8c903cbbf |
completed | May 10, 2026, 11:57 p.m. |
Created at: April 10, 2026, 5:33 a.m.