Triple

T2253144
Position Surface form Disambiguated ID Type / Status
Subject National Library of the Kingdom of Morocco E49660 entity
Predicate shortName P43 FINISHED
Object BNRM
BNRM is the National Library of the Kingdom of Morocco, serving as the country’s main institution for preserving and providing access to its written and documentary heritage.
 E247155 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: BNRM | Statement: [National Library of the Kingdom of Morocco, shortName, BNRM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: BNRM
Context triple: [National Library of the Kingdom of Morocco, shortName, BNRM]
  • A. BN
    BN is the vehicle registration code used on license plates for the German city of Bonn.
  • B. .bn
    .bn is the country code top-level domain (ccTLD) assigned to Brunei Darussalam for use in its internet addresses.
  • C. BAM
    BAM is a renowned multi-arts center in Brooklyn, New York, known for its innovative programming in theater, dance, music, opera, and film.
  • D. BRL
    BRL is the official currency code for the Brazilian real, the legal tender of Brazil.
  • E. BR2
    BR2 is a UK postcode district covering parts of Hayes and surrounding areas in the London Borough of Bromley in southeast England.
  • 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: BNRM
Triple: [National Library of the Kingdom of Morocco, shortName, BNRM]
Generated description
BNRM is the National Library of the Kingdom of Morocco, serving as the country’s main institution for preserving and providing access to its written and documentary heritage.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: BNRM
Target entity description: BNRM is the National Library of the Kingdom of Morocco, serving as the country’s main institution for preserving and providing access to its written and documentary heritage.
  • A. BN
    BN is the vehicle registration code used on license plates for the German city of Bonn.
  • B. .bn
    .bn is the country code top-level domain (ccTLD) assigned to Brunei Darussalam for use in its internet addresses.
  • C. BAM
    BAM is a renowned multi-arts center in Brooklyn, New York, known for its innovative programming in theater, dance, music, opera, and film.
  • D. BRL
    BRL is the official currency code for the Brazilian real, the legal tender of Brazil.
  • E. BR2
    BR2 is a UK postcode district covering parts of Hayes and surrounding areas in the London Borough of Bromley in southeast England.
  • 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_69a88aaa9250819095e127d0d77e8a32 completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc12029548190af9f2cdd7a4de2d6 completed March 7, 2026, 6:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae6b1dd6fc8190bd762fb3a17258b0 completed March 9, 2026, 6:39 a.m.
NEDg Description generation batch_69ae6bbdef14819084b96389435ca080 completed March 9, 2026, 6:42 a.m.
NED2 Entity disambiguation (via description) batch_69ae6c2cfac48190b0425088e79cd122 completed March 9, 2026, 6:43 a.m.
Created at: March 4, 2026, 7:47 p.m.