Triple

T15217920
Position Surface form Disambiguated ID Type / Status
Subject Corinna Cortes E363686 entity
Predicate notableWork P4 FINISHED
Object Support-Vector Networks (1995) 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 (1995) | Statement: [Corinna Cortes, notableWork, Support-Vector Networks (1995)]

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 (1995)
Context triple: [Corinna Cortes, notableWork, Support-Vector Networks (1995)]
  • 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. 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.
  • D. libsvm
    libsvm is a widely used open-source library that implements Support Vector Machines for classification, regression, and related machine learning tasks.
  • E. Gradient-based learning applied to document recognition
    "Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
  • 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_69fedd3159fc81908c05cfbd0bd7e5ac ned_source_triple completed
Created at: April 10, 2026, 3:11 a.m.