Triple
T4651118
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Transformer |
E102296
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | encoder-decoder Transformer |
E435886
|
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: encoder-decoder Transformer | Statement: [Transformer, hasVariant, encoder-decoder Transformer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: encoder-decoder Transformer Context triple: [Transformer, hasVariant, encoder-decoder Transformer]
-
A.
EncoderDecoderModel
chosen
EncoderDecoderModel is a Hugging Face Transformers architecture that combines a separate encoder and decoder into a unified sequence-to-sequence model for tasks like translation, summarization, and text generation.
-
B.
VisionEncoderDecoderModel
VisionEncoderDecoderModel is a Hugging Face Transformers architecture that combines a vision encoder with a text decoder to perform tasks like image captioning and visual question answering.
-
C.
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.
-
D.
Transformer
Transformer is a neural network architecture based on self-attention mechanisms that has become the foundation for modern large language models and many state-of-the-art systems in natural language processing.
-
E.
DeBERTa
DeBERTa is a transformer-based language model developed by Microsoft that improves upon BERT and RoBERTa using disentangled attention and enhanced mask decoder mechanisms for superior natural language understanding.
- 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_69bdfae7636881908244b86cba1c66b7 |
completed | March 21, 2026, 1:56 a.m. |
Created at: March 20, 2026, 1:14 p.m.