Triple

T11003242
Position Surface form Disambiguated ID Type / Status
Subject Long-term Recurrent Convolutional Networks for Visual Recognition and Description E260050 entity
Predicate relatedTo P37 FINISHED
Object LSTM E814035 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: LSTM | Statement: [Long-term Recurrent Convolutional Networks for Visual Recognition and Description, relatedTo, LSTM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LSTM
Context triple: [Long-term Recurrent Convolutional Networks for Visual Recognition and Description, relatedTo, LSTM]
  • A. LSTM networks chosen
    LSTM networks are a type of recurrent neural network architecture designed to effectively capture long-term dependencies in sequential data by using gated memory cells.
  • B. GRU
    GRU is the IATA airport code for São Paulo–Guarulhos International Airport, the main international gateway serving São Paulo, Brazil.
  • C. GRU
    GRU is Russia’s military intelligence agency, known for conducting espionage, cyber operations, and covert activities abroad.
  • D. Row LSTM
    Row LSTM is a recurrent neural network architecture used in PixelRNN that processes images row by row to model spatial dependencies for generative image modeling.
  • E. Connectionist Temporal Classification
    Connectionist Temporal Classification is a neural network training algorithm designed for sequence labeling tasks where input and output lengths differ and alignments are unknown, widely used in speech and handwriting recognition.
  • 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.