Triple
T31600897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monetary Policy Committee of the Central Bank of Brazil |
E806344
|
entity |
| Predicate | typeOfRateSet |
P9385
|
FINISHED |
| Object | policy interest rate |
—
|
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: policy interest rate | Statement: [Monetary Policy Committee of the Central Bank of Brazil, typeOfRateSet, policy interest rate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfRateSet Context triple: [Monetary Policy Committee of the Central Bank of Brazil, typeOfRateSet, policy interest rate]
-
A.
hasRateType
chosen
Indicates the specific category or scheme under which a rate (such as a price, fee, or interest) is defined or applied.
-
B.
typeOfSets
Indicates that one entity specifies or classifies the kind or category of sets to which another entity belongs.
-
C.
usesRatesSetBy
Indicates that one entity applies or follows the pricing or rate structure determined by another entity.
-
D.
typeOfRules
Indicates that one entity specifies or categorizes the kind or category of rules that apply to or are associated with another entity.
-
E.
typeOfSettlement
Indicates the specific category or classification of a settlement (e.g., city, town, village) that characterizes what kind of settlement it is.
- 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_69f348d54ccc8190a03b5df9a2b40b25 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fff09dae088190bd8460060d778feb |
completed | May 10, 2026, 2:42 a.m. |
| PD | Predicate disambiguation | batch_69fff0027c5c8190baa5c7a15852cbe0 |
completed | May 10, 2026, 2:40 a.m. |
Created at: April 30, 2026, 10:32 p.m.