Triple

T11003260
Position Surface form Disambiguated ID Type / Status
Subject Distributed Representations of Sentences and Documents E260051 entity
Predicate proposesMethod P859 FINISHED
Object Paragraph Vector Distributed Bag of Words E899023 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: Paragraph Vector Distributed Bag of Words | Statement: [Distributed Representations of Sentences and Documents, proposesMethod, Paragraph Vector Distributed Bag of Words]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Paragraph Vector Distributed Bag of Words
Context triple: [Distributed Representations of Sentences and Documents, proposesMethod, Paragraph Vector Distributed Bag of Words]
  • A. Paragraph Vector chosen
    Paragraph Vector is an unsupervised learning algorithm that generates fixed-length vector representations for variable-length texts such as sentences, paragraphs, and documents, enabling them to be used effectively in machine learning tasks.
  • B. 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.
  • C. 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.
  • D. 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.
  • E. Apache Mahout
    Apache Mahout is an open-source machine learning library designed to build scalable algorithms for clustering, classification, and recommendation on large datasets, often leveraging big data platforms.
  • 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_69d6aa8a6a548190a750f944ccdc8064 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d797546f448190946ee6442d657dc5 completed April 9, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69e37486b23081909ad282397c50a913 completed April 18, 2026, 12:09 p.m.
Created at: April 8, 2026, 9:25 p.m.