Triple
T19693053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Llama |
E472883
|
entity |
| Predicate | hasVersion |
P455
|
FINISHED |
| Object | Llama 3 |
—
|
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 | Statement: [Llama, hasVersion, Llama 3]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Llama 3 Context triple: [Llama, hasVersion, Llama 3]
-
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.
Lala
Lala is an Indian honorific title traditionally used as a respectful prefix for educated or distinguished men, particularly in North India.
-
C.
Lala
Lala is a small town in the Hailakandi district of the Indian state of Assam.
-
D.
Lama glama
Lama glama is the domesticated South American camelid commonly known as the llama, used as a pack animal and for its wool.
-
E.
Tikkana
Tikkana was a prominent 13th-century Telugu poet and scholar best known for translating a major portion of the Mahabharata into Telugu and helping shape classical Telugu literature.
- 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.