Triple

T7874884
Position Surface form Disambiguated ID Type / Status
Subject Layer Normalization E182824 entity
Predicate usedIn P98 FINISHED
Object 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.
 E701503 NE FINISHED

How this triple was built (4 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: [Layer Normalization, usedIn, Transformer-XL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Transformer-XL
Context triple: [Layer Normalization, usedIn, Transformer-XL]
  • A. 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.
  • B. Longformer
    Longformer is a transformer-based neural network architecture designed for efficient processing of very long sequences using sparse attention mechanisms.
  • C. 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.
  • D. 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.
  • 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. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Transformer-XL
Triple: [Layer Normalization, usedIn, Transformer-XL]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Transformer-XL
Target entity description: 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.
  • A. 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.
  • B. Longformer
    Longformer is a transformer-based neural network architecture designed for efficient processing of very long sequences using sparse attention mechanisms.
  • C. 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.
  • D. 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.
  • 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. chosen

Provenance (5 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_69ca828a17248190b46defe758bc5ad3 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb39a961188190b2f12f8fe5d66641 completed March 31, 2026, 3:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5b79705c8190955e128081048ebe completed March 31, 2026, 5:28 a.m.
NEDg Description generation batch_69cb7630b8908190a0b8f4856bceea0a completed March 31, 2026, 7:22 a.m.
NED2 Entity disambiguation (via description) batch_69cbbfb894588190971ade076acdbd5c completed March 31, 2026, 12:36 p.m.
Created at: March 30, 2026, 4:56 p.m.