Triple
T8611998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Open Roberta |
E203932
|
entity |
| Predicate | supportsDevice |
P5090
|
FINISHED |
| Object | Calliope mini |
E40792
|
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: Calliope mini | Statement: [Open Roberta, supportsDevice, Calliope mini]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Calliope mini Context triple: [Open Roberta, supportsDevice, Calliope mini]
-
A.
Calliope mini
chosen
Calliope mini is a small educational microcontroller board designed to teach children and beginners programming and electronics through interactive projects.
-
B.
Calliope
Calliope is the Muse of epic poetry in Greek mythology, often depicted as the chief of the nine Muses and associated with eloquence and heroic verse.
-
C.
Raspberry Pi Pico
Raspberry Pi Pico is a low-cost, microcontroller-based development board from the Raspberry Pi Foundation built around the RP2040 chip for embedded and hobbyist projects.
-
D.
Gemini Nano
Gemini Nano is a lightweight, on-device variant of Google’s Gemini AI model designed to run efficiently on mobile and edge devices.
-
E.
Crusoe microprocessor
The Crusoe microprocessor is a low-power, x86-compatible CPU line from Transmeta that used code-morphing software to translate x86 instructions to an underlying VLIW architecture, targeting laptops and mobile devices.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca832c23e4819095a9f3eea4a21828 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc46fc31e08190aab5ab8f92f3315c |
completed | March 31, 2026, 10:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cea91456a88190a7416b0f1a0327d6 |
completed | April 2, 2026, 5:36 p.m. |
Created at: March 30, 2026, 6:25 p.m.