Triple

T11108954
Position Surface form Disambiguated ID Type / Status
Subject Tomas Mikolov E262703 entity
Predicate developed P73 FINISHED
Object word2vec algorithm E906310 NE FINISHED

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: word2vec algorithm | Statement: [Tomas Mikolov, developed, word2vec algorithm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: word2vec algorithm
Context triple: [Tomas Mikolov, developed, word2vec algorithm]
  • A. word2vec chosen
    word2vec is a neural network-based technique for learning dense vector representations of words that capture semantic and syntactic relationships, widely used in natural language processing.
  • B. Efficient Estimation of Word Representations in Vector Space
    Efficient Estimation of Word Representations in Vector Space is the influential 2013 paper that introduced the word2vec models for learning distributed word embeddings, significantly advancing natural language processing.
  • C. Deep contextualized word representations
    Deep contextualized word representations is a seminal NLP paper that introduced ELMo, a deep bidirectional language model that produces context-sensitive word embeddings and significantly advanced performance on many language understanding tasks.
  • D. Latent Dirichlet Allocation
    Latent Dirichlet Allocation is a generative probabilistic model commonly used in natural language processing to discover latent topics within large collections of documents.
  • E. Distributed Representations of Sentences and Documents
    "Distributed Representations of Sentences and Documents" is a seminal machine learning paper that introduced the Paragraph Vector (Doc2Vec) method for learning continuous vector representations of variable-length text such as sentences, paragraphs, and documents.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6aa9b46cc8190b19f9f0cc45bf322 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d79a67d10c8190815d4c27d55270e8 completed April 9, 2026, 12:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69e441c3b1788190868fae6919544149 completed April 19, 2026, 2:45 a.m.
Created at: April 8, 2026, 9:27 p.m.