Triple
T18205454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hugging Face Accelerate |
E435889
|
entity |
| Predicate | supportsBackend |
P15794
|
FINISHED |
| Object | Megatron-LM |
—
|
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: Megatron-LM | Statement: [Hugging Face Accelerate, supportsBackend, Megatron-LM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Megatron-LM Context triple: [Hugging Face Accelerate, supportsBackend, Megatron-LM]
-
A.
Megatron-LM
chosen
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.
-
B.
LLaMA
LLaMA is a family of large language models developed by Meta AI, designed for efficient training and inference across a range of natural language processing tasks.
-
C.
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.
-
D.
PaLM 2
PaLM 2 is a large-scale language model developed by Google, known for powering various AI features across Google products before being succeeded by the Gemini family of models.
-
E.
DeepScale
DeepScale was an AI startup focused on efficient deep learning and computer vision models for resource-constrained devices, particularly in the automotive and embedded systems space.
- 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_69e4e2234b988190bbe2c2164d61f65f |
completed | April 19, 2026, 2:09 p.m. |
Created at: April 10, 2026, 10:32 a.m.