Triple

T2163783
Position Surface form Disambiguated ID Type / Status
Subject Banco de la República E46860 entity
Predicate shortName P43 FINISHED
Object Banrep
Banrep is the commonly used abbreviation for Banco de la República, Colombia’s central bank responsible for monetary policy and currency issuance.
 E239042 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: Banrep | Statement: [Banco de la República, shortName, Banrep]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Banrep
Context triple: [Banco de la República, shortName, Banrep]
  • A. Dender
    The Dender is a river in Belgium that flows through Wallonia and Flanders before joining the Scheldt near the city of Dendermonde.
  • B. Arwad
    Arwad is an ancient Phoenician island city-state off the coast of modern-day Syria, historically known as a significant maritime and trading center in the eastern Mediterranean.
  • C. Tahawus
    Tahawus is a remote hamlet in New York’s Adirondack Mountains known for its historic iron mining operations and proximity to High Peaks wilderness areas.
  • D. Rekhetre
    Rekhetre was an ancient Egyptian queen of the 4th Dynasty, known primarily as one of the wives of Pharaoh Menkaure.
  • E. Hamura
    Hamura is a city in western Tokyo, Japan, known for its residential neighborhoods and proximity to the Tama River.
  • 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: Banrep
Triple: [Banco de la República, shortName, Banrep]
Generated description
Banrep is the commonly used abbreviation for Banco de la República, Colombia’s central bank responsible for monetary policy and currency issuance.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Banrep
Target entity description: Banrep is the commonly used abbreviation for Banco de la República, Colombia’s central bank responsible for monetary policy and currency issuance.
  • A. Dender
    The Dender is a river in Belgium that flows through Wallonia and Flanders before joining the Scheldt near the city of Dendermonde.
  • B. Arwad
    Arwad is an ancient Phoenician island city-state off the coast of modern-day Syria, historically known as a significant maritime and trading center in the eastern Mediterranean.
  • C. Tahawus
    Tahawus is a remote hamlet in New York’s Adirondack Mountains known for its historic iron mining operations and proximity to High Peaks wilderness areas.
  • D. Rekhetre
    Rekhetre was an ancient Egyptian queen of the 4th Dynasty, known primarily as one of the wives of Pharaoh Menkaure.
  • E. Hamura
    Hamura is a city in western Tokyo, Japan, known for its residential neighborhoods and proximity to the Tama River.
  • 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_69a88a184cbc8190877791f6552c2484 completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abbe8d105c819098371c35c88873dc completed March 7, 2026, 5:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae58ee18ac81909f02e2c87000365b completed March 9, 2026, 5:21 a.m.
NEDg Description generation batch_69ae597198b88190b0253aa121ed35e1 completed March 9, 2026, 5:24 a.m.
NED2 Entity disambiguation (via description) batch_69ae5a02404c819088acf7c592cb2cae completed March 9, 2026, 5:26 a.m.
Created at: March 4, 2026, 7:45 p.m.