Triple
T18205385
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AutoConfig |
E435887
|
entity |
| Predicate | compatibleWith |
P203
|
FINISHED |
| Object | AutoModelForCausalLM |
—
|
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: AutoModelForCausalLM | Statement: [AutoConfig, compatibleWith, AutoModelForCausalLM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AutoModelForCausalLM Context triple: [AutoConfig, compatibleWith, AutoModelForCausalLM]
-
A.
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.
-
B.
Hugging Face Transformers
chosen
Hugging Face Transformers is a widely used open-source library that provides state-of-the-art transformer-based models and tools for natural language processing and related machine learning tasks.
-
C.
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.
-
D.
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.
-
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.
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.