Triple

T2518706
Position Surface form Disambiguated ID Type / Status
Subject Ghana Museums and Monuments Board E55472 entity
Predicate headquartersLocation P62 FINISHED
Object Accra E68377 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: Accra | Statement: [Ghana Museums and Monuments Board, headquartersLocation, Accra]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Accra
Context triple: [Ghana Museums and Monuments Board, headquartersLocation, Accra]
  • A. Accra chosen
    Accra is the capital and largest city of Ghana, known as a major economic, political, and cultural hub in West Africa.
  • B. Kumasi
    Kumasi is a major city in southern Ghana, known as the historic capital of the Ashanti Kingdom and a key cultural and commercial center in West Africa.
  • C. Serekunda
    Serekunda is the most populous urban center and a major commercial hub in The Gambia.
  • D. Ashaiman
    Ashaiman is a densely populated urban municipality in southern Ghana that functions as a major residential and commercial hub near the capital, Accra.
  • E. Freetown
    Freetown is the capital and largest city of Sierra Leone, known as a historic port and former center for resettled freed slaves in West 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_69ab49e4749c8190813311efd1630f1b completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd232ae188190b7b70806b466ee99 completed March 7, 2026, 7:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69af2b9ef8048190bafff3f1853321cb completed March 9, 2026, 8:20 p.m.
Created at: March 6, 2026, 9:46 p.m.