Triple
T22813758
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TMVA |
E565047
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | TMVA::Factory |
—
|
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: TMVA::Factory | Statement: [TMVA, hasComponent, TMVA::Factory]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TMVA::Factory Context triple: [TMVA, hasComponent, TMVA::Factory]
-
A.
TMVA
chosen
TMVA (Toolkit for Multivariate Data Analysis) is a ROOT-integrated machine learning framework widely used in high-energy physics for classification, regression, and multivariate analysis tasks.
-
B.
LIBLINEAR
LIBLINEAR is an open-source machine learning library specialized for large-scale linear classification and regression, particularly efficient for linear SVMs and logistic regression.
-
C.
TensorFlow Decision Forests
TensorFlow Decision Forests is a TensorFlow library for training, serving, and interpreting decision forest models such as random forests and gradient-boosted trees.
-
D.
CatBoost
CatBoost is an open-source gradient boosting library developed by Yandex, optimized for handling categorical features and delivering high-performance machine learning models.
-
E.
Svm
Svm is the station code used to identify Svanemøllen railway station in Copenhagen’s public transport system.
- 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_69e2458426188190b58b8ab4844fe420 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17d62b0ec8190ac22909192e8a876 |
completed | April 29, 2026, 3:39 a.m. |
Created at: April 17, 2026, 3:32 p.m.