Triple
T10438064
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RON |
E246092
|
entity |
| Predicate | fractionalUnitCode |
P94039
|
FINISHED |
| Object | BAN |
—
|
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: BAN | Statement: [RON, fractionalUnitCode, BAN]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fractionalUnitCode Context triple: [RON, fractionalUnitCode, BAN]
-
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.
minorUnitsPerUnit
Indicates the number of smaller sub-units that collectively make up one whole unit in a given measurement or currency system.
-
D.
unitFractionalName
Indicates that one entity is the name or label used to represent a fractional unit of another entity (such as a measurement or quantity).
-
E.
minorUnitExponent
Indicates the power-of-ten exponent that defines how a minor unit relates in scale to its corresponding major unit.
- 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_69d381bf3dc08190bf35a2643e4e8f22 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4fe083cd881909d2d8ad75d1d94cb |
completed | April 7, 2026, 12:52 p.m. |
| PD | Predicate disambiguation | batch_69d4fb73a5e48190a8df4775bc5da80f |
completed | April 7, 2026, 12:41 p.m. |
| PDg | Predicate description generation | batch_69d4fe058fcc81909428137d9ffd6d90 |
completed | April 7, 2026, 12:52 p.m. |
Created at: April 6, 2026, 12:14 p.m.