Triple

T841600
Position Surface form Disambiguated ID Type / Status
Subject Zambia E18189 entity
Predicate capital P234 FINISHED
Object Lusaka E31817 NE FINISHED

How this triple was built (2 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: Lusaka | Statement: [Zambia, capital, Lusaka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lusaka
Context triple: [Zambia, capital, Lusaka]
  • A. Lusaka, Zambia chosen
    Lusaka, Zambia is the capital and largest city of Zambia, serving as the country’s political, economic, and cultural center.
  • B. Kinshasa
    Kinshasa is the largest city and political, economic, and cultural center of the Democratic Republic of the Congo, located along the Congo River in Central Africa.
  • C. Bulawayo
    Bulawayo is Zimbabwe’s second-largest city and a major industrial, cultural, and transport hub in the southwestern part of the country.
  • D. Luanda
    Luanda is the capital and largest city of Angola, a major Atlantic port and economic hub with a history shaped by Portuguese colonial rule and the transatlantic slave trade.
  • E. Malabo
    Malabo is the largest city and main economic and administrative center of Equatorial Guinea, located on the northern coast of Bioko Island in the Gulf of Guinea.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69a49389f44881909a608fb27d89f247 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4abe6f0dc8190a1bebb5e21f4ceac completed March 1, 2026, 9:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7929a91088190bef474424bde527c completed March 4, 2026, 2:02 a.m.
Created at: March 1, 2026, 7:38 p.m.