Triple

T4651223
Position Surface form Disambiguated ID Type / Status
Subject GPT-3 E102297 entity
Predicate describedIn P519 FINISHED
Object Language Models are Few-Shot Learners E457860 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: Language Models are Few-Shot Learners | Statement: [GPT-3, describedIn, 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: [GPT-3, describedIn, 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. LLaMA
    LLaMA is a family of large language models developed by Meta AI, designed for efficient training and inference across a range of natural language processing tasks.
  • E. LLM
    LLM is the ICAO airline designator assigned to Yamal Airlines, a Russian regional carrier.
  • 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_69bd43d71a308190afea7280841b0de8 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd630343f88190954d19fcd18a5864 completed March 20, 2026, 3:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69be0374967c8190b77bcd3ea1c4d59d completed March 21, 2026, 2:33 a.m.
Created at: March 20, 2026, 1:14 p.m.