Triple

T18724117
Position Surface form Disambiguated ID Type / Status
Subject Noam Shazeer E457852 entity
Predicate coAuthorOf P2389 FINISHED
Object Exploring the Limits of Language Modeling — 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: Exploring the Limits of Language Modeling | Statement: [Noam Shazeer, coAuthorOf, Exploring the Limits of Language Modeling]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Exploring the Limits of Language Modeling
Context triple: [Noam Shazeer, coAuthorOf, Exploring the Limits of Language Modeling]
  • A. Exploring the Limits of Language Modeling chosen
    "Exploring the Limits of Language Modeling" is a research paper that investigates how far large-scale neural language models can be pushed in terms of performance, scalability, and generalization on natural language tasks.
  • B. 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.
  • C. Language Models are Few-Shot Learners
    "Language Models are Few-Shot Learners" is a landmark research paper that demonstrated large-scale transformer-based language models can perform diverse tasks from just a few examples without task-specific training.
  • D. Extensions of recurrent neural network language model
    "Extensions of Recurrent Neural Network Language Model" is a research work by Tomas Mikolov that advances neural language modeling by improving and extending recurrent neural network architectures for better performance in natural language processing tasks.
  • 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_69d8d393ba9c8190a8b03b04ddbb0a09 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e56abcfc048190a01dee959e768768 completed April 19, 2026, 11:52 p.m.
Created at: April 10, 2026, 11:50 a.m.