Triple
T20106405
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gemini Pro |
E490189
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Google Gemini models |
—
|
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: Google Gemini models | Statement: [Gemini Pro, partOf, Google Gemini models]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Google Gemini models Context triple: [Gemini Pro, partOf, Google Gemini models]
-
A.
Google Gemini
chosen
Google Gemini is Google's family of advanced multimodal AI models designed to handle text, code, images, and other data types for a wide range of intelligent applications.
-
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-3
GPT-3 is a large-scale autoregressive language model known for generating human-like text and performing a wide range of natural language tasks with minimal fine-tuning.
-
D.
Cohere
Cohere is an artificial intelligence company known for developing large language models and NLP platforms for enterprises.
-
E.
AI21 Labs
AI21 Labs is an artificial intelligence company specializing in large language models and advanced natural language processing technologies.
- 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_69da62636cc08190982cc71733a17b8d |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e666dcb8d4819091889e19dd9137a6 |
completed | April 20, 2026, 5:48 p.m. |
Created at: April 11, 2026, 11:28 p.m.