Triple
T18205306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EncoderDecoderModel |
E435886
|
entity |
| Predicate | canUseDecoderType |
P9928
|
FINISHED |
| Object | GPT2LMHeadModel |
—
|
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: GPT2LMHeadModel | Statement: [EncoderDecoderModel, canUseDecoderType, GPT2LMHeadModel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GPT2LMHeadModel Context triple: [EncoderDecoderModel, canUseDecoderType, GPT2LMHeadModel]
-
A.
GPT-2
chosen
GPT-2 is a large transformer-based language model known for generating coherent, human-like text and sparking widespread discussion about the implications of advanced AI text generation.
-
B.
Megatron-LM
Megatron-LM is a large-scale language model training framework developed by NVIDIA, designed to efficiently train massive transformer models through model, tensor, and pipeline parallelism.
-
C.
BertLMHeadModel
BertLMHeadModel is a BERT-based language modeling architecture that adds a prediction head on top of BERT to generate or score text token sequences.
-
D.
GPT-J
GPT-J is an open-source, large-scale autoregressive language model developed by EleutherAI as a high-quality alternative to proprietary models like OpenAI's GPT-3.
-
E.
GPT-Neo
GPT-Neo is an open-source family of autoregressive language models developed by EleutherAI as a free alternative to OpenAI’s GPT-3.
- 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: canUseDecoderType Context triple: [EncoderDecoderModel, canUseDecoderType, GPT2LMHeadModel]
-
A.
usesCodec
Indicates that one entity employs or relies on a specific codec to encode, decode, or process data.
-
B.
canUse
chosen
Indicates that one entity has the ability, permission, or suitability to make use of another entity or resource.
-
C.
decodingMethod
Indicates the technique or process used to convert encoded or encrypted data back into its original, interpretable form.
-
D.
hasTypeOfSupport
Indicates that one entity provides or is associated with a particular kind or category of support in relation to another entity.
-
E.
canUseNetworkType
Indicates that an entity is permitted or able to operate using a specified type of network.
- F. None of above.
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_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. |
Created at: April 10, 2026, 10:32 a.m.