Triple
T1774585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NuBus |
E38948
|
entity |
| Predicate | isEndiannessAgnostic |
P31520
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [NuBus, isEndiannessAgnostic, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isEndiannessAgnostic Context triple: [NuBus, isEndiannessAgnostic, true]
-
A.
endianness
Indicates the ordering of bytes used to represent multi-byte data values in memory or storage.
-
B.
endianess
Indicates the byte order relationship specifying how multi-byte data is arranged in memory or during transmission (e.g., little-endian vs big-endian).
-
C.
hasDigitalEncoding
Indicates that one entity is represented, stored, or expressed using a specific digital code or encoding scheme provided by another entity.
-
D.
hasDistinctCharacterSet
Indicates that two compared items use different sets of characters, with no character set being a subset or duplicate of the other.
-
E.
isRealValued
Indicates that the value or function in question takes values exclusively from the set of real numbers.
- F. None of above. chosen
Provenance (4 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_69a8862e61708190af97b9838cc3f5de |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab17e368048190b7b73d156400f772 |
completed | March 6, 2026, 6:07 p.m. |
| PD | Predicate disambiguation | batch_69aa61cd4c1c8190a8dff391f5642bfe |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab17d0a644819087e6ce39d6c60da5 |
completed | March 6, 2026, 6:07 p.m. |
Created at: March 4, 2026, 7:31 p.m.