Triple

T1277205
Position Surface form Disambiguated ID Type / Status
Subject Africa/Blantyre E27241 entity
Predicate linkedTo P37 FINISHED
Object Africa/Maputo E70168 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: Africa/Maputo | Statement: [Africa/Blantyre, linkedTo, Africa/Maputo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Africa/Maputo
Context triple: [Africa/Blantyre, linkedTo, Africa/Maputo]
  • A. Beira
    Beira is a major port city in central Mozambique, serving as a key commercial and transport hub for the region.
  • B. Port of Maputo
    The Port of Maputo is Mozambique’s principal deep-water seaport and a major regional hub for maritime trade in southeastern Africa.
  • C. 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.
  • D. Lourenço Marques
    Lourenço Marques is the former name of Maputo, the capital city and main port of Mozambique.
  • E. Mozambique
    Mozambique is a southeastern African nation on the Indian Ocean known for its Portuguese colonial heritage, rich cultural diversity, and extensive coastline with important ports and marine resources.
  • 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_69a496d3710c8190955dee8bc0dacb50 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c0907f6081908df15679227341b5 completed March 1, 2026, 10:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69aca2f6c8a08190ac6b1f477388adbb completed March 7, 2026, 10:13 p.m.
Created at: March 1, 2026, 7:50 p.m.