Triple

T848972
Position Surface form Disambiguated ID Type / Status
Subject GPT-2 E18339 entity
Predicate paperTitle P38 FINISHED
Object Language Models are Unsupervised Multitask Learners E18339 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 Unsupervised Multitask Learners | Statement: [GPT-2, paperTitle, Language Models are Unsupervised Multitask Learners]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Language Models are Unsupervised Multitask Learners
Context triple: [GPT-2, paperTitle, Language Models are Unsupervised Multitask Learners]
  • A. GPT-2 chosen
    GPT-2 is a large transformer-based language model known for generating coherent, human-like text and sparking widespread discussion about the implications of advanced AI text generation.
  • B. GPT-3
    GPT-3 is a large-scale autoregressive language model known for generating human-like text and performing a wide range of natural language tasks with minimal fine-tuning.
  • C. CLIP
    CLIP is an OpenAI model that learns joint representations of images and text, enabling tasks like zero-shot image classification and natural language-based image retrieval.
  • D. Automatic Adam
    Automatic Adam is the nickname of Adam Vinatieri, a legendary NFL placekicker renowned for his clutch, game-winning field goals in high-pressure situations.
  • E. Generative Adversarial Networks
    Generative Adversarial Networks are a class of machine learning models in which two neural networks compete to generate highly realistic synthetic data, such as images, audio, or text.
  • 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_69a4938b04208190b82e1df6b572c548 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4ac1fac3481909cba7070ce31a9b3 completed March 1, 2026, 9:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69a792a0666c8190bfc9166d45b4e867 completed March 4, 2026, 2:02 a.m.
Created at: March 1, 2026, 7:38 p.m.