Triple

T12665859
Position Surface form Disambiguated ID Type / Status
Subject Goma E302550 entity
Predicate roadConnectionTo P9041 FINISHED
Object Butembo E477184 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: Butembo | Statement: [Goma, roadConnectionTo, Butembo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Butembo
Context triple: [Goma, roadConnectionTo, Butembo]
  • A. Butembo chosen
    Butembo is a major commercial city in eastern Democratic Republic of the Congo, known as a trading hub and economic center in North Kivu.
  • B. Butiama
    Butiama is a village in northern Tanzania best known as the birthplace and hometown of the country’s founding president, Julius Nyerere.
  • C. Uvira
    Uvira is a city in the eastern Democratic Republic of the Congo, located on the northern shores of Lake Tanganyika near the border with Burundi.
  • D. Ekondo-Titi
    Ekondo-Titi is a coastal town and commune in Cameroon's Southwest Region, known for its agricultural activities and location near the Ndian River and the Atlantic coast.
  • E. Moanda
    Moanda is a major mining town in southeastern Gabon known for its rich manganese deposits and role in the country’s extractive industry.
  • 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_69d7bded71a88190bb76e2413af9ea66 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d9617e030881908444743b8a7e0d75 completed April 10, 2026, 8:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69f684dfce0c8190b3b8260450bd6967 completed May 2, 2026, 11:12 p.m.
Created at: April 9, 2026, 5:19 p.m.