Triple
T816189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MicroPython |
E17655
|
entity |
| Predicate | typicalRAMFootprint |
P1931
|
FINISHED |
| Object | tens of kilobytes |
—
|
LITERAL 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: tens of kilobytes | Statement: [MicroPython, typicalRAMFootprint, tens of kilobytes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalRAMFootprint Context triple: [MicroPython, typicalRAMFootprint, tens of kilobytes]
-
A.
hasRAM
Indicates that an entity possesses or is equipped with a specified amount or type of random-access memory (RAM).
-
B.
minRAM
Indicates that an entity requires at least a specified minimum amount of RAM to function or be considered valid.
-
C.
memoryType
Indicates the specific category or kind of memory associated with an entity or process.
-
D.
primaryMemoryType
Indicates the main or dominant type of memory associated with or used by an entity in a given context.
-
E.
typicalCapacity
chosen
Indicates the usual or standard amount, volume, or capability that something is designed or expected to hold, handle, or perform under normal conditions.
- F. None of above.
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_69a4937bcaac8190a322524ac6f45a5a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ab5157b08190b6c8f2fd455f261e |
completed | March 1, 2026, 9:10 p.m. |
| PD | Predicate disambiguation | batch_69a4aa756920819080ae82948974c876 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:38 p.m.