Triple
T4277336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PCA (scikit-learn) |
E97073
|
entity |
| Predicate | svd_solverOption |
P21840
|
FINISHED |
| Object | auto |
—
|
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: auto | Statement: [PCA (scikit-learn), svd_solverOption, auto]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: svd_solverOption Context triple: [PCA (scikit-learn), svd_solverOption, auto]
-
A.
typicalRank
Indicates the usual or most common rank or position an entity holds within a given ordering or hierarchy.
-
B.
typicalRankRange
Indicates the usual or most common range of ranks or ordered positions that an entity typically occupies within a ranking or hierarchy.
-
C.
reconstructionMethod
Indicates the technique or process used to reconstruct, restore, or rebuild something from its original or fragmented state.
-
D.
algorithmType
chosen
Indicates the specific kind or category of algorithm associated with an entity or process.
-
E.
numberOfUnknowns
Indicates the count of variables or elements in a situation, equation, or problem whose values are not yet determined or specified.
- F. None of above.
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_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. |
Created at: March 12, 2026, 11:07 p.m.