Triple
T4277340
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PCA (scikit-learn) |
E97073
|
entity |
| Predicate | whitenEffect |
P55164
|
FINISHED |
| Object | scales components to unit variance |
—
|
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: scales components to unit variance | Statement: [PCA (scikit-learn), whitenEffect, scales components to unit variance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: whitenEffect Context triple: [PCA (scikit-learn), whitenEffect, scales components to unit variance]
-
A.
visualEffect
Indicates that one entity produces, modifies, or is associated with a particular visual effect on another entity or within a scene.
-
B.
whitePoint
Indicates the reference color point or standard white used as a basis for color measurements or calibration in a color space.
-
C.
blackAndWhite
Indicates that something is presented or exists in only black and white, without any other colors.
-
D.
noiseReductionFeature
Indicates that an entity includes or supports a capability to reduce or minimize unwanted noise.
-
E.
textureTreatment
Indicates how an entity’s surface feel or texture has been modified, processed, or treated.
- 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3501ef1388190b0c968b069014a59 |
completed | March 12, 2026, 11:45 p.m. |
| PD | Predicate disambiguation | batch_69b347faa45481908c19c29fb906dc92 |
completed | March 12, 2026, 11:10 p.m. |
| PDg | Predicate description generation | batch_69b34e0606488190baadf469a1afc3c2 |
completed | March 12, 2026, 11:36 p.m. |
Created at: March 12, 2026, 11:07 p.m.