Triple

T11003262
Position Surface form Disambiguated ID Type / Status
Subject Distributed Representations of Sentences and Documents E260051 entity
Predicate abbreviation P43 FINISHED
Object PV-DBOW E260051 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: PV-DBOW | Statement: [Distributed Representations of Sentences and Documents, abbreviation, PV-DBOW]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: PV-DBOW
Context triple: [Distributed Representations of Sentences and Documents, abbreviation, PV-DBOW]
  • A. DSSM
    DSSM is the post-nominal abbreviation used by recipients of the U.S. Defense Superior Service Medal, a high-level military decoration awarded for superior meritorious service in a position of significant responsibility.
  • B. Distributed Representations of Sentences and Documents chosen
    "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. Pointer Networks
    Pointer Networks are a type of neural network architecture that uses attention mechanisms to output discrete positions in an input sequence, enabling solutions to combinatorial problems like sorting and the traveling salesman problem.
  • D. Diagonal BiLSTM
    Diagonal BiLSTM is a recurrent neural network architecture used in PixelRNN models to efficiently capture two-dimensional spatial dependencies in images by processing pixels along diagonals with bidirectional LSTMs.
  • E. Neural Discrete Representation Learning
    Neural Discrete Representation Learning is a machine learning framework that introduces Vector Quantized Variational Autoencoders (VQ-VAE) to learn discrete latent representations for high-dimensional data such as images, audio, and video.
  • 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_69e3453d181081908cb58a957f4d1295 completed April 18, 2026, 8:47 a.m.
Created at: April 8, 2026, 9:25 p.m.