Triple
T14009081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | rupee |
E337029
|
entity |
| Predicate | typicalSubunitRatio |
P507
|
FINISHED |
| Object | 1 rupee = 100 paisa |
—
|
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: 1 rupee = 100 paisa | Statement: [rupee, typicalSubunitRatio, 1 rupee = 100 paisa]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSubunitRatio Context triple: [rupee, typicalSubunitRatio, 1 rupee = 100 paisa]
-
A.
subunitRatio
chosen
Indicates the proportional relationship between the quantities or sizes of different subunits within a larger whole.
-
B.
typicalSubmultiples
Indicates that one quantity represents a standard or commonly used fractional multiple of another quantity (e.g., milli-, micro-, kilo- as typical submultiples).
-
C.
usesSameSubunitStructureAs
Indicates that two entities share an identical or equivalent arrangement and composition of their constituent subunits within a larger structural framework.
-
D.
typicalUnitSize
Indicates the standard or most common size or quantity in which something is typically measured, packaged, or used.
-
E.
subunitType
Indicates that one entity is a specific kind or classification of subunit within the structure or composition of another entity.
- 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_69d81c645c5c8190b1fd16a285a1b78a |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2ed44f90819099ad08c09c066b56 |
completed | April 14, 2026, 12:11 p.m. |
| PD | Predicate disambiguation | batch_69dd465dfbc4819090d8c61fd572d35f |
completed | April 13, 2026, 7:39 p.m. |
Created at: April 9, 2026, 10:19 p.m.