Triple
T19693066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Llama |
E472883
|
entity |
| Predicate | hasSubModel |
P43872
|
FINISHED |
| Object | Llama 3.2 11B |
—
|
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 11B | Statement: [Llama, hasSubModel, Llama 3.2 11B]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Llama 3.2 11B Context triple: [Llama, hasSubModel, Llama 3.2 11B]
-
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.
LLM
LLM (Large Language Model) is an advanced artificial intelligence system trained on vast text datasets to understand and generate human-like language for a wide range of tasks.
-
D.
LLM
LLM is the commonly used abbreviation for the Liechtenstein National Museum, the principal cultural and historical museum of Liechtenstein.
-
E.
LLM
LLM is the ICAO airline designator assigned to Yamal Airlines, a Russian regional carrier.
- 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.