Triple
T19693067
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Llama |
E472883
|
entity |
| Predicate | hasSubModel |
P43872
|
FINISHED |
| Object | Llama 3.2 90B |
—
|
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: Llama 3.2 90B | Statement: [Llama, hasSubModel, Llama 3.2 90B]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Llama 3.2 90B Context triple: [Llama, hasSubModel, Llama 3.2 90B]
-
A.
LLaMA
chosen
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.
-
B.
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.
-
C.
GPT-NeoX-20B
GPT-NeoX-20B is a 20-billion-parameter open-source large language model developed by EleutherAI as a powerful successor to the GPT-Neo family for advanced text generation and research.
-
D.
GPT-3.5
GPT-3.5 is a large language model that generates human-like text and powers conversational AI applications such as advanced chatbots and coding assistants.
-
E.
Falcon-7B-Instruct
Falcon-7B-Instruct is an instruction-tuned 7-billion-parameter variant of the Falcon large language model, optimized for following user prompts in natural language tasks.
- 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_69d8e515bef88190bc30781aea50537a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e64211e5d481908358d922e0dca271 |
completed | April 20, 2026, 3:11 p.m. |
Created at: April 10, 2026, 1:46 p.m.