Triple
T18205309
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EncoderDecoderModel |
E435886
|
entity |
| Predicate | canUseDecoderType |
P9928
|
FINISHED |
| Object | XLNetLMHeadModel |
—
|
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: XLNetLMHeadModel | Statement: [EncoderDecoderModel, canUseDecoderType, XLNetLMHeadModel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: XLNetLMHeadModel Context triple: [EncoderDecoderModel, canUseDecoderType, XLNetLMHeadModel]
-
A.
XLNet
chosen
XLNet is a generalized autoregressive pretraining model for natural language processing that improves on BERT by leveraging permutation-based language modeling to better capture bidirectional context.
-
B.
XLM-R
XLM-R is a multilingual transformer-based language model (XLM-RoBERTa) designed for cross-lingual understanding and natural language processing across many languages.
-
C.
Transformer-XL
Transformer-XL is a neural network architecture for language modeling that extends the Transformer with segment-level recurrence and relative positional encodings to better capture long-range dependencies.
-
D.
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.
-
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 (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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e222831081908f7d5500424e3acb |
completed | April 19, 2026, 2:09 p.m. |
Created at: April 10, 2026, 10:32 a.m.