Triple

T14870032
Position Surface form Disambiguated ID Type / Status
Subject Maputo Railway Station E349718 entity
Predicate connectsTo P845 FINISHED
Object South Africa E3669 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: South Africa | Statement: [Maputo Railway Station, connectsTo, South Africa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: South Africa
Context triple: [Maputo Railway Station, connectsTo, South Africa]
  • A. South Africa chosen
    South Africa is a country at the southern tip of the African continent, known for its cultural and linguistic diversity, complex history of apartheid and democratic transition, and significant economic and political influence in the region.
  • B. Transkei, South Africa
    Transkei, South Africa was a former bantustan in the southeastern part of the country, historically designated for Xhosa-speaking people during the apartheid era.
  • C. Kwaluseni
    Kwaluseni is a town in Eswatini known primarily as the main campus site of the University of Eswatini.
  • D. Tiszanána
    Tiszanána is a village in northern Hungary known for its proximity to the Tisza River and recreational areas around Lake Tisza.
  • E. Vereeniging, South Africa
    Vereeniging is an industrial city in South Africa’s Gauteng province, historically known for its steel and coal industries and its role in the country’s mining and manufacturing economy.
  • 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_69d822ee4f408190b6ac3b2fa434f0df completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded57967748190a7c05ebb74aacb7c completed April 15, 2026, 12:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe64f8b4148190bc24f9a307178419 completed May 8, 2026, 10:34 p.m.
Created at: April 10, 2026, 1:55 a.m.