Triple
T22813717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TMVA |
E565047
|
entity |
| Predicate | acronym |
P43
|
FINISHED |
| Object | TMVA |
—
|
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 | Statement: [TMVA, acronym, TMVA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TMVA Context triple: [TMVA, acronym, TMVA]
-
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.
Svm
Svm is the station code used to identify Svanemøllen railway station in Copenhagen’s public transport system.
-
C.
SMVDM
SMVDM is a hands-on children's museum in California’s Santa Maria Valley that offers interactive exhibits and educational programs focused on science, technology, and local culture.
-
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.
libsvm
libsvm is a widely used open-source library that implements Support Vector Machines for classification, regression, and related machine learning tasks.
- 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.