Triple
T3946850
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Omani rial |
E92166
|
entity |
| Predicate | isDivisibleInto |
P31032
|
FINISHED |
| Object | baisa subunits |
—
|
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: baisa subunits | Statement: [Omani rial, isDivisibleInto, baisa subunits]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isDivisibleInto Context triple: [Omani rial, isDivisibleInto, baisa subunits]
-
A.
isDivisible
Indicates that one quantity can be evenly divided by another without leaving a remainder.
-
B.
isDividedBy
Indicates that one quantity or entity serves as the divisor that evenly or proportionally separates another quantity or entity into parts.
-
C.
usesDivisor
Indicates that one entity employs another entity as a divisor in a division or modular arithmetic operation.
-
D.
isDivisibleUnitOf
chosen
Indicates that one unit can be evenly divided into another unit, such that the second unit is an exact multiple or fraction of the first.
-
E.
dividedBy
Indicates that one quantity is separated into a specified number of equal parts or groups by another quantity, representing a division relationship between them.
- 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_69aed965502c8190904ebad1203a4ae8 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef1515c688190a38332aedeed8a76 |
completed | March 9, 2026, 4:12 p.m. |
| PD | Predicate disambiguation | batch_69aee764235081909309b3c982f322a9 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:24 p.m.