Triple

T2818142
Position Surface form Disambiguated ID Type / Status
Subject Congolese franc E54339 entity
Predicate replacedCurrency P2867 FINISHED
Object new zaire
The new zaire was the former currency of the Democratic Republic of the Congo that circulated during the late Mobutu era before being replaced amid severe inflation and economic reform.
 E300907 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: new zaire | Statement: [Congolese franc, replacedCurrency, new zaire]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: new zaire
Context triple: [Congolese franc, replacedCurrency, new zaire]
  • A. Nzera
    Nzera is a settlement located within Tanzania’s Geita Region in East Africa.
  • B. Nzega
    Nzega is a town and district in western Tanzania that serves as an important commercial and transport hub within the Tabora Region.
  • C. Muanda, Democratic Republic of the Congo
    Muanda is a coastal town and oil port in the far west of the Democratic Republic of the Congo, situated on the Atlantic Ocean close to the country’s short coastline.
  • D. Ndzebi
    Ndzebi is a Bantu language spoken primarily by the Nzebi people of Gabon and neighboring regions.
  • E. Luba
    Luba is a coastal town and important port on the southern part of Bioko Island in Equatorial Guinea.
  • 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: new zaire
Triple: [Congolese franc, replacedCurrency, new zaire]
Generated description
The new zaire was the former currency of the Democratic Republic of the Congo that circulated during the late Mobutu era before being replaced amid severe inflation and economic reform.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: new zaire
Target entity description: The new zaire was the former currency of the Democratic Republic of the Congo that circulated during the late Mobutu era before being replaced amid severe inflation and economic reform.
  • A. Nzera
    Nzera is a settlement located within Tanzania’s Geita Region in East Africa.
  • B. Nzega
    Nzega is a town and district in western Tanzania that serves as an important commercial and transport hub within the Tabora Region.
  • C. Muanda, Democratic Republic of the Congo
    Muanda is a coastal town and oil port in the far west of the Democratic Republic of the Congo, situated on the Atlantic Ocean close to the country’s short coastline.
  • D. Ndzebi
    Ndzebi is a Bantu language spoken primarily by the Nzebi people of Gabon and neighboring regions.
  • E. Luba
    Luba is a coastal town and important port on the southern part of Bioko Island in Equatorial Guinea.
  • 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_69ab49de0af08190b3da69683be1e728 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abde6c44d881909f8275b6466e2f20 completed March 7, 2026, 8:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69afcea4c994819095611958936cf090 completed March 10, 2026, 7:56 a.m.
NEDg Description generation batch_69afcf12e3a0819098f28d31434a0c5f completed March 10, 2026, 7:58 a.m.
NED2 Entity disambiguation (via description) batch_69afcf9c2d308190b111aa8038c9227a completed March 10, 2026, 8 a.m.
Created at: March 6, 2026, 9:59 p.m.