Triple
T18204361
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | XLNet |
E435866
|
entity |
| Predicate | extends |
P1244
|
FINISHED |
| Object | Transformer-XL |
—
|
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: Transformer-XL | Statement: [XLNet, extends, Transformer-XL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Transformer-XL Context triple: [XLNet, extends, Transformer-XL]
-
A.
Transformer-XL
chosen
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.
-
B.
XLNet
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.
-
C.
Longformer
Longformer is a transformer-based neural network architecture designed for efficient processing of very long sequences using sparse attention mechanisms.
-
D.
Attention Is All You Need
"Attention Is All You Need" is the landmark 2017 research paper that introduced the Transformer architecture and revolutionized modern natural language processing and sequence modeling.
-
E.
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.
- 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.