Triple

T20955666
Position Surface form Disambiguated ID Type / Status
Subject Lusophone Africa E516093 entity
Predicate hasMajorCity P316 FINISHED
Object Maputo NE NERFINISHED

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: Maputo | Statement: [Lusophone Africa, hasMajorCity, Maputo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maputo
Context triple: [Lusophone Africa, hasMajorCity, Maputo]
  • A. Maputo chosen
    Maputo is the largest city and main economic and cultural center of Mozambique, located on the country’s southern coast along the Indian Ocean.
  • B. Tshwane
    Tshwane is a major metropolitan area in South Africa that includes the country’s administrative capital, Pretoria, and serves as an important political and economic hub.
  • 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. Bloemfontein
    Bloemfontein is a major South African city known as the seat of the country’s highest courts and one of its three national capitals.
  • E. Mbombela
    Mbombela is a city in northeastern South Africa that serves as a regional economic and administrative hub near the border with Mozambique.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4fcd678819087a304291f14330a completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6fb6b19048190b266ed24f6fc1a97 completed April 21, 2026, 4:22 a.m.
Created at: April 16, 2026, 1:28 p.m.