Triple
T18204420
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | T5 |
E435867
|
entity |
| Predicate | usesEncoder |
P130213
|
FINISHED |
| Object | Transformer encoder |
—
|
NE NERFINISHED |
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: Transformer encoder | Statement: [T5, usesEncoder, Transformer encoder]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Transformer encoder Context triple: [T5, usesEncoder, Transformer encoder]
-
A.
Transformer encoder-only
chosen
A Transformer encoder-only model is a neural network architecture that uses only the encoder stack of the Transformer to process input sequences, typically for tasks like classification, retrieval, and masked language modeling.
-
B.
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.
-
C.
Transformer
Transformer is a 1972 glam rock album by Lou Reed, co-produced by David Bowie and Mick Ronson, known for its influential sound and iconic tracks like "Walk on the Wild Side."
-
D.
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.
-
E.
EncoderDecoderModel
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.
- 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: usesEncoder Context triple: [T5, usesEncoder, Transformer encoder]
-
A.
usesCodec
Indicates that one entity employs or relies on a specific codec to encode, decode, or process data.
-
B.
encodes
Indicates that one entity contains or represents the information, instructions, or structure of another in a coded or symbolic form.
-
C.
encodedIn
Indicates that one entity is represented, stored, or expressed within another entity using a specific encoding or format.
-
D.
imageEncoderType
Indicates the specific kind or configuration of encoder used to process and represent image data.
-
E.
notEncodedIn
Indicates that a piece of information, data, or content is explicitly absent from or not represented within a given encoding, format, or medium.
- F. None of above. chosen
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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e222831081908f7d5500424e3acb |
completed | April 19, 2026, 2:09 p.m. |
| PD | Predicate disambiguation | batch_69e4332155d88190b106d0dceb4554af |
completed | April 19, 2026, 1:42 a.m. |
| PDg | Predicate description generation | batch_69e438f684e48190b38c64b58c518b6a |
completed | April 19, 2026, 2:07 a.m. |
Created at: April 10, 2026, 10:32 a.m.