Triple

T4372232
Position Surface form Disambiguated ID Type / Status
Subject Venezuelan bolívar E98922 entity
Predicate formerISO4217Code P15239 FINISHED
Object VEF
VEF was the ISO 4217 currency code for the Venezuelan bolívar used before its redenomination to the bolívar soberano.
 E435398 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: VEF | Statement: [Venezuelan bolívar, formerISO4217Code, VEF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: VEF
Context triple: [Venezuelan bolívar, formerISO4217Code, VEF]
  • A. VE
    VE is the two-letter ISO 3166-1 alpha-2 country code assigned to Venezuela for international standardization and identification purposes.
  • B. VELO
    VELO is the high-precision vertex detector of the LHCb experiment at CERN, designed to measure particle trajectories very close to the proton–proton collision point.
  • C. VIF
    VIF is the commonly used abbreviation and nickname for Vålerenga Fotball, a Norwegian professional football club based in Oslo.
  • D. HVF
    HVF is a data-focused startup and innovation lab created by entrepreneur Max Levchin to explore and build companies around large-scale data problems.
  • E. VEN
    VEN is the three-letter ISO 3166-1 alpha-3 country code assigned to Venezuela for international identification and data standards.
  • 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: VEF
Triple: [Venezuelan bolívar, formerISO4217Code, VEF]
Generated description
VEF was the ISO 4217 currency code for the Venezuelan bolívar used before its redenomination to the bolívar soberano.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: VEF
Target entity description: VEF was the ISO 4217 currency code for the Venezuelan bolívar used before its redenomination to the bolívar soberano.
  • A. VE
    VE is the two-letter ISO 3166-1 alpha-2 country code assigned to Venezuela for international standardization and identification purposes.
  • B. VELO
    VELO is the high-precision vertex detector of the LHCb experiment at CERN, designed to measure particle trajectories very close to the proton–proton collision point.
  • C. VIF
    VIF is the commonly used abbreviation and nickname for Vålerenga Fotball, a Norwegian professional football club based in Oslo.
  • D. HVF
    HVF is a data-focused startup and innovation lab created by entrepreneur Max Levchin to explore and build companies around large-scale data problems.
  • E. VEN
    VEN is the three-letter ISO 3166-1 alpha-3 country code assigned to Venezuela for international identification and data standards.
  • 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_69b3454db3708190aeafd814413c4c3d completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3521dffbc8190b9300a7f4f64bdc0 completed March 12, 2026, 11:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5e50ec35481908cf1e1afffda19cb completed March 14, 2026, 10:45 p.m.
NEDg Description generation batch_69b5e5e96aac819093c43dc355de4509 completed March 14, 2026, 10:49 p.m.
NED2 Entity disambiguation (via description) batch_69b5e6587be88190884f61a7350ce5a9 completed March 14, 2026, 10:51 p.m.
Created at: March 12, 2026, 11:17 p.m.