Triple
T17561668
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | XGBoost |
E427706
|
entity |
| Predicate | supportsDataFormat |
P8463
|
FINISHED |
| Object | LibSVM format |
—
|
NE NERFINISHED |
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: LibSVM format | Statement: [XGBoost, supportsDataFormat, LibSVM format]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LibSVM format Context triple: [XGBoost, supportsDataFormat, LibSVM format]
-
A.
libsvm
chosen
libsvm is a widely used open-source library that implements Support Vector Machines for classification, regression, and related machine learning tasks.
-
B.
Svm
Svm is the station code used to identify Svanemøllen railway station in Copenhagen’s public transport system.
-
C.
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.
-
D.
Chih-Chung Chang and Chih-Jen Lin
Chih-Chung Chang and Chih-Jen Lin are computer scientists best known for developing LIBSVM, a widely used open-source library for support vector machines in machine learning.
-
E.
LinearClassifier
LinearClassifier is a TensorFlow Estimator that implements a linear model for classification tasks, typically using features combined with linear weights to predict discrete labels.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889e0385081908a04b66f4dd4bd0d |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e456274c888190ac80402e391674dd |
completed | April 19, 2026, 4:12 a.m. |
Created at: April 10, 2026, 5:50 a.m.