Triple

T15816858
Position Surface form Disambiguated ID Type / Status
Subject Royal Monetary Authority of Bhutan E383501 entity
Predicate abbreviation P43 FINISHED
Object RMA
RMA is the central bank of Bhutan responsible for issuing currency, managing monetary policy, and overseeing the country’s financial system.
 E1178421 NE FINISHED

How this triple was built (4 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: RMA | Statement: [Royal Monetary Authority of Bhutan, abbreviation, RMA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: RMA
Context triple: [Royal Monetary Authority of Bhutan, abbreviation, RMA]
  • A. RMA
    RMA is a U.S. Department of Agriculture agency responsible for overseeing federal crop insurance and risk management programs for farmers and ranchers.
  • B. RMA
    RMA is the IATA airport code for Roma Airport, a regional airport serving the town of Roma in Queensland, Australia.
  • C. RMA
    RMA is New Zealand’s principal environmental and resource management law that governs how natural and physical resources are used, developed, and protected.
  • D. RM
    RM is a UK postcode area in east London and parts of Essex, covering districts such as Romford and surrounding suburbs.
  • E. RM
    RM is the currency symbol that was used to denote the German Reichsmark, the former official currency of Germany from 1924 to 1948.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: RMA
Triple: [Royal Monetary Authority of Bhutan, abbreviation, RMA]
Generated description
RMA is the central bank of Bhutan responsible for issuing currency, managing monetary policy, and overseeing the country’s financial system.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: RMA
Target entity description: RMA is the central bank of Bhutan responsible for issuing currency, managing monetary policy, and overseeing the country’s financial system.
  • A. RMA
    RMA is a U.S. Department of Agriculture agency responsible for overseeing federal crop insurance and risk management programs for farmers and ranchers.
  • B. RMA
    RMA is New Zealand’s principal environmental and resource management law that governs how natural and physical resources are used, developed, and protected.
  • C. RMA
    RMA is the IATA airport code for Roma Airport, a regional airport serving the town of Roma in Queensland, Australia.
  • D. RM
    RM is a UK postcode area in east London and parts of Essex, covering districts such as Romford and surrounding suburbs.
  • E. RM
    RM is the currency symbol that was used to denote the German Reichsmark, the former official currency of Germany from 1924 to 1948.
  • F. None of above. chosen

Provenance (5 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_69d86da2858c819090cc8481e7207b6e completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e0c4a306a48190840adc49df2c26c5 completed April 16, 2026, 11:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff99959f048190ae24a072387ec233 completed May 9, 2026, 8:31 p.m.
NEDg Description generation batch_69ff9ad6b29081909ff2abb2c4d866a4 completed May 9, 2026, 8:36 p.m.
NED2 Entity disambiguation (via description) batch_69ff9b443280819088dbf18f7c57406b completed May 9, 2026, 8:38 p.m.
Created at: April 10, 2026, 4:49 a.m.