Triple
T17035313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tucker decomposition |
E413306
|
entity |
| Predicate | factorMatrixProperty |
P125586
|
FINISHED |
| Object | contains mode-specific latent factors |
—
|
LITERAL 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: contains mode-specific latent factors | Statement: [Tucker decomposition, factorMatrixProperty, contains mode-specific latent factors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: factorMatrixProperty Context triple: [Tucker decomposition, factorMatrixProperty, contains mode-specific latent factors]
-
A.
factor
Indicates that one entity is a contributing cause, influence, or component affecting the state, outcome, or existence of another entity.
-
B.
isProportionalityFactorIn
Indicates that one quantity serves as the proportionality factor (constant of proportionality) in a specified proportional relationship or equation.
-
C.
determinantProperty
Indicates that one property or factor determines, governs, or decisively influences another property or outcome.
-
D.
hasMathematicalProperty
Indicates that one entity possesses or exhibits a specific mathematical property or characteristic.
-
E.
numberOfIndependentMatrices
Indicates the count of matrices in a set that are linearly independent from each other.
- F. None of above. chosen
Provenance (4 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. |
| PD | Predicate disambiguation | batch_69e35d5be7f48190af9db67a1e23850f |
completed | April 18, 2026, 10:30 a.m. |
| PDg | Predicate description generation | batch_69e3753f93c88190808fec5692f66699 |
completed | April 18, 2026, 12:12 p.m. |
Created at: April 10, 2026, 5:33 a.m.