Triple
T871358
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GPT-3 |
E18819
|
entity |
| Predicate | describedInPaper |
P519
|
FINISHED |
| Object | Language Models are Few-Shot Learners |
E18819
|
NE FINISHED |
How this triple was built (3 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, describedInPaper, 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, describedInPaper, Language Models are Few-Shot Learners]
-
A.
GPT-3
chosen
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.
-
B.
Hugging Face Transformers
Hugging Face Transformers is a widely used open-source library that provides state-of-the-art transformer-based models and tools for natural language processing and related machine learning tasks.
-
C.
GPT-2
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.
-
D.
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.
-
E.
GPT-3.5
GPT-3.5 is a large language model that generates human-like text and powers conversational AI applications such as advanced chatbots and coding assistants.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: describedInPaper Context triple: [GPT-3, describedInPaper, Language Models are Few-Shot Learners]
-
A.
describedIn
chosen
Indicates that information about an entity is contained or documented within a specified source, such as a text, document, or media.
-
B.
describes
Indicates that one entity provides an explanation, representation, or account of another entity or concept.
-
C.
presentedIn
Indicates that something is shown, displayed, or formally introduced within a particular context, medium, event, or setting.
-
D.
hasDescription
Indicates that an entity is associated with a textual description that explains or characterizes it.
-
E.
usedInManuscripts
Indicates that something (such as a term, symbol, or feature) appears or is employed within one or more manuscripts.
- F. None of above.
Provenance (4 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_69a4938db1f081909bcd1ad2713b6096 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac96850881908a2d776685126137 |
completed | March 1, 2026, 9:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7a3cb9a648190981182add42325f3 |
completed | March 4, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69a4aa89ca008190b50d061ac7fe19f9 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:39 p.m.