Triple
T31955373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NVIDIA RTX 4000 SFF Ada Generation |
E815889
|
entity |
| Predicate | supportsTechnology |
P5090
|
FINISHED |
| Object |
NVIDIA Mosaic
NVIDIA Mosaic is a display technology that enables seamless spanning of a single desktop or application across multiple monitors to create large, high-resolution visual workspaces or video walls.
|
E1985217
|
NE FINISHED |
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: NVIDIA Mosaic | Statement: [NVIDIA RTX 4000 SFF Ada Generation, supportsTechnology, NVIDIA Mosaic]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: NVIDIA Mosaic Triple: [NVIDIA RTX 4000 SFF Ada Generation, supportsTechnology, NVIDIA Mosaic]
Generated description
NVIDIA Mosaic is a display technology that enables seamless spanning of a single desktop or application across multiple monitors to create large, high-resolution visual workspaces or video walls.
Provenance (5 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_69f348f4ec708190abbb2a7c3ed58844 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b2ae50e08190b0756dfe533618ea |
completed | May 3, 2026, 2:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2e8a4ff3cc8190b085ba11c656c7cf |
completed | June 14, 2026, 11:02 a.m. |
| NEDg | Description generation | batch_6a2e8af50dc08190bbc5f3e04528b335 |
completed | June 14, 2026, 11:05 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a2ea6a967dc8190af1d4e84ed34013b |
completed | June 14, 2026, 1:03 p.m. |
Created at: May 1, 2026, 12:08 a.m.