Triple

T6762739
Position Surface form Disambiguated ID Type / Status
Subject Ronga E154633 entity
Predicate influences P9 FINISHED
Object Maputo Portuguese E22780 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: Maputo Portuguese | Statement: [Ronga, influences, Maputo Portuguese]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maputo Portuguese
Context triple: [Ronga, influences, Maputo Portuguese]
  • A. African Portuguese chosen
    African Portuguese is the group of regional varieties of the Portuguese language spoken across several African countries, shaped by local languages and cultures.
  • B. Maputo
    Maputo is the largest city and main economic and cultural center of Mozambique, located on the country’s southern coast along the Indian Ocean.
  • C. Tshivenda
    Tshivenda is a Bantu language spoken primarily by the Venda people in northern South Africa and neighboring regions.
  • D. Beira
    Beira is a major port city in central Mozambique, serving as a key commercial and transport hub for the region.
  • E. 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.
  • 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_69c688109c1c8190added9a221292af0 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d2160a2c8190837c608a3509c62c completed March 27, 2026, 6:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69c712b6ec408190bd9131f289b02ba7 completed March 27, 2026, 11:28 p.m.
Created at: March 27, 2026, 2:12 p.m.