Triple
T36308031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mauritius Turf Club |
E893992
|
entity |
| Predicate | usesCurrencyForBetting |
P103526
|
FINISHED |
| Object | Mauritian rupee |
—
|
NE NERFINISHED |
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: Mauritian rupee | Statement: [Mauritius Turf Club, usesCurrencyForBetting, Mauritian rupee]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesCurrencyForBetting Context triple: [Mauritius Turf Club, usesCurrencyForBetting, Mauritian rupee]
-
A.
usesCurrency
Indicates that one entity conducts its financial transactions or values using the monetary unit represented by the other entity.
-
B.
currencyUsedInCasino
chosen
Indicates that a particular type of currency is accepted and used for gambling transactions within a casino.
-
C.
usesCurrentCurrenciesOf
Indicates that one entity adopts and operates with the same official currencies that are currently in use in another entity.
-
D.
usesCurrencyInitially
Indicates that an entity originally adopts or operates with a particular currency at the start of a defined period or process.
-
E.
usesBettingStructure
Indicates that one entity employs or follows a particular betting structure in the context of wagering or games.
- 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_69f76e4c1b248190b10667d0213537fe |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fe0d165a48819098b854318a50d76c |
completed | May 8, 2026, 4:19 p.m. |
| PD | Predicate disambiguation | batch_69fe0931002481908a95b34f95e9f64e |
completed | May 8, 2026, 4:02 p.m. |
Created at: May 3, 2026, 4:09 p.m.