Triple
T4277205
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SVC |
E97071
|
entity |
| Predicate | implementedInLibrary |
P55160
|
FINISHED |
| Object | scikit-learn |
E17661
|
NE FINISHED |
How this triple was built (3 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: scikit-learn | Statement: [SVC, implementedInLibrary, scikit-learn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: scikit-learn Context triple: [SVC, implementedInLibrary, scikit-learn]
-
A.
scikit-learn
chosen
scikit-learn is a widely used open-source Python library that provides efficient tools for data mining, data analysis, and implementing a broad range of machine learning algorithms.
-
B.
libsvm
libsvm is a widely used open-source library that implements Support Vector Machines for classification, regression, and related machine learning tasks.
-
C.
LogisticRegression
LogisticRegression is a scikit-learn machine learning estimator that models the probability of class membership using a linear decision boundary with logistic (sigmoid) or related link functions.
-
D.
Support Vector Machines
Support Vector Machines are a class of supervised learning algorithms used primarily for classification and regression tasks, which work by finding the optimal separating hyperplane between data classes in a high-dimensional feature space.
-
E.
Apache Mahout
Apache Mahout is an open-source machine learning library designed to build scalable algorithms for clustering, classification, and recommendation on large datasets, often leveraging big data platforms.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: implementedInLibrary Context triple: [SVC, implementedInLibrary, scikit-learn]
-
A.
implementedInLanguage
Indicates that a piece of software or code is written using a particular programming language.
-
B.
hasOfficialLibrary
Indicates that an entity possesses or is served by a formally recognized, authorized library.
-
C.
canBeImplementedWith
Indicates that one entity is capable of being realized, executed, or fulfilled through the use or application of another entity.
-
D.
implementedOn
Indicates that something (such as a feature, standard, or specification) is realized, executed, or put into effect within or on a particular platform, system, or environment.
-
E.
commonlyImplementedBy
Indicates that the referenced item (e.g., a standard, interface, or pattern) is frequently realized or put into practice by the associated implementing entities.
- F. None of above. chosen
Provenance (5 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. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5e4e7a7cc8190a2ffc15c236f80d5 |
completed | March 14, 2026, 10:44 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.