Triple

T18204336
Position Surface form Disambiguated ID Type / Status
Subject XLNet E435866 entity
Predicate proposedInPaper P35415 FINISHED
Object XLNet: Generalized Autoregressive Pretraining for Language Understanding — NE NERFINISHED

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: XLNet: Generalized Autoregressive Pretraining for Language Understanding | Statement: [XLNet, proposedInPaper, XLNet: Generalized Autoregressive Pretraining for Language Understanding]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: XLNet: Generalized Autoregressive Pretraining for Language Understanding
Context triple: [XLNet, proposedInPaper, XLNet: Generalized Autoregressive Pretraining for Language Understanding]
  • A. XLNet chosen
    XLNet is a generalized autoregressive pretraining model for natural language processing that improves on BERT by leveraging permutation-based language modeling to better capture bidirectional context.
  • B. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
    "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" is the seminal research paper that introduced the T5 model, framing all NLP tasks in a unified text-to-text format and demonstrating state-of-the-art transfer learning performance across diverse benchmarks.
  • C. Transformer-XL
    Transformer-XL is a neural network architecture for language modeling that extends the Transformer with segment-level recurrence and relative positional encodings to better capture long-range dependencies.
  • D. Language Models are Unsupervised Multitask Learners
    "Language Models are Unsupervised Multitask Learners" is a 2019 OpenAI research paper that demonstrated how large-scale unsupervised language models like GPT-2 can perform a wide range of tasks without task-specific training.
  • E. OPT: Open Pre-trained Transformer Language Models
    OPT: Open Pre-trained Transformer Language Models is a family of openly released large-scale transformer-based language models developed by Meta AI to provide transparent, reproducible alternatives to proprietary models like GPT-3.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b90dba6481908e119eb9aa4ca0cb completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4e222831081908f7d5500424e3acb completed April 19, 2026, 2:09 p.m.
Created at: April 10, 2026, 10:32 a.m.