Triple
T446253
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bermudian dollar |
E7026
|
entity |
| Predicate | fractionalUnit |
P6798
|
FINISHED |
| Object | cent |
—
|
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: cent | Statement: [Bermudian dollar, fractionalUnit, cent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fractionalUnit Context triple: [Bermudian dollar, fractionalUnit, cent]
-
A.
fractionalUnitSymbol
Indicates the symbolic notation used to represent the fractional unit associated with a given quantity or measurement.
-
B.
fractionalUnitNameInFrench
Indicates the French-language name used for a fractional unit associated with another quantity or measure.
-
C.
unitFractionalName
chosen
Indicates that one entity is the name or label used to represent a fractional unit of another entity (such as a measurement or quantity).
-
D.
hasFraction
Indicates that one entity represents a fractional part or proportion of another entity.
-
E.
minorUnitUsage
Indicates how a minor or subordinate unit is used or functions in relation to a larger or primary unit.
- 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_69a2e7e4676c81909ea0dbdecac0687c |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ef62c7a88190851fcd57658b4102 |
completed | Feb. 28, 2026, 1:36 p.m. |
| PD | Predicate disambiguation | batch_69a2eddfb5508190a4e06e1b260d8b2b |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.