Triple
T26242277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lattice ECP5 |
E656346
|
entity |
| Predicate | iOPinsRange |
P11992
|
FINISHED |
| Object | up to about 365 user I/Os (device dependent) |
—
|
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: up to about 365 user I/Os (device dependent) | Statement: [Lattice ECP5, iOPinsRange, up to about 365 user I/Os (device dependent)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: iOPinsRange Context triple: [Lattice ECP5, iOPinsRange, up to about 365 user I/Os (device dependent)]
-
A.
pinCount
chosen
Indicates the number of pins associated with or assigned to a given entity.
-
B.
pioCount
Indicates the number of PIO (e.g., process I/O or peripheral I/O) operations or units associated with an entity.
-
C.
hasNumberOfPins
Indicates that an entity is associated with a specific count of pins it possesses or uses.
-
D.
dataPinCount
Indicates the number of data pins associated with or used by an entity in a given context.
-
E.
hasPinCount
Indicates that an entity is associated with a specific number of pins.
- 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_69ee5b4c59a881909d9ee4fd013fffd5 |
completed | April 26, 2026, 6:37 p.m. |
| NER | Named-entity recognition | batch_69f60d8f7f9c8190bb8cb8f8ac4c0ca5 |
completed | May 2, 2026, 2:43 p.m. |
| PD | Predicate disambiguation | batch_69f5f7fd90fc81909055b211368f9139 |
completed | May 2, 2026, 1:11 p.m. |
Created at: April 26, 2026, 9:04 p.m.