Triple

T15217901
Position Surface form Disambiguated ID Type / Status
Subject Corinna Cortes E363686 entity
Predicate coAuthorOf P2389 FINISHED
Object Support-Vector Networks E426671 NE FINISHED

Named-entity recognition

Before disambiguation, gpt-5-mini classified whether the object phrase is a named entity — the step behind the object's NE type shown above.

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: Support-Vector Networks | Statement: [Corinna Cortes, coAuthorOf, Support-Vector Networks]

Disambiguation candidates (1 decision)

The exact options the model was shown at each disambiguation step, with the option it chose highlighted — the evidence behind this triple's disambiguated ids.

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Support-Vector Networks
Context triple: [Corinna Cortes, coAuthorOf, Support-Vector Networks]
  • A. Support Vector Machines chosen
    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.
  • B. Svm
    Svm is the station code used to identify Svanemøllen railway station in Copenhagen’s public transport system.
  • C. libsvm
    libsvm is a widely used open-source library that implements Support Vector Machines for classification, regression, and related machine learning tasks.
  • D. Probably Approximately Correct learning (PAC learning)
    Probably Approximately Correct (PAC) learning is a foundational framework in computational learning theory that formalizes what it means for an algorithm to efficiently learn a concept from examples with high probability and small error.
  • E. Bayesian learning for neural networks
    Bayesian learning for neural networks is an approach that applies Bayesian inference to neural network models, treating their weights as probability distributions to improve uncertainty estimation and generalization.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

Stage Batch ID Job type Status
creating batch_69d85a0ce24c81909c4d3b6475548c95 elicitation completed
NER batch_69e0076f90c481909989befe031a2cae ner completed
NED1 batch_69fed345d58c81908a8fd182c0fe7c15 ned_source_triple completed
Created at: April 10, 2026, 3:11 a.m.