Triple
T721775
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tunisian dinar |
E14631
|
entity |
| Predicate | hasSubunitFraction |
P507
|
FINISHED |
| Object | 1/1000 |
—
|
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: 1/1000 | Statement: [Tunisian dinar, hasSubunitFraction, 1/1000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubunitFraction Context triple: [Tunisian dinar, hasSubunitFraction, 1/1000]
-
A.
hasFraction
Indicates that one entity represents a fractional part or proportion of another entity.
-
B.
subunitRatio
chosen
Indicates the proportional relationship between the quantities or sizes of different subunits within a larger whole.
-
C.
hasSubdivision
Indicates that one entity is divided into and contains another entity as one of its constituent parts or administrative units.
-
D.
fractionalUnitSymbol
Indicates the symbolic notation used to represent the fractional unit associated with a given quantity or measurement.
-
E.
formerSubunit
Indicates that one entity was previously a subunit or subordinate part of another entity, but no longer holds that status.
- 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_69a4934c753c81909b309027e48b9b3a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a591124c8190842e7ef18b064198 |
completed | March 1, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69a4a4f513608190b716b939d574c292 |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:37 p.m.