Triple

T21302242
Position Surface form Disambiguated ID Type / Status
Subject Eric Sigler E525096 entity
Predicate notableWork P4 FINISHED
Object Language Models are Few-Shot Learners — 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: Language Models are Few-Shot Learners | Statement: [Eric Sigler, notableWork, Language Models are Few-Shot Learners]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Language Models are Few-Shot Learners
Context triple: [Eric Sigler, notableWork, Language Models are Few-Shot Learners]
  • A. Language Models are Few-Shot Learners chosen
    "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.
  • 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. Exploring the Limits of Language Modeling
    "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.
  • D. 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.
  • E. 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.
  • 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_69e0b517e6748190850d6f6ddf323d69 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e7385cd6308190bf300494833b048f completed April 21, 2026, 8:42 a.m.
Created at: April 16, 2026, 4:05 p.m.