Triple

T220599
Position Surface form Disambiguated ID Type / Status
Subject Akademi Kreyòl Ayisyen E4203 entity
Predicate countryCapital P204 FINISHED
Object Port-au-Prince E29318 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: Port-au-Prince | Statement: [Akademi Kreyòl Ayisyen, countryCapital, Port-au-Prince]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Port-au-Prince
Context triple: [Akademi Kreyòl Ayisyen, countryCapital, Port-au-Prince]
  • A. Port-au-Prince chosen
    Port-au-Prince is the capital and largest city of Haiti, serving as the country’s political, economic, and cultural center.
  • B. Panama City
    Panama City is the largest urban center and economic hub of Panama, known for its modern skyline, historic Casco Viejo district, and proximity to the Panama Canal.
  • C. San Salvador
    San Salvador is the largest city of El Salvador and its political, cultural, and economic center.
  • D. Tegucigalpa
    Tegucigalpa is the capital and largest city of Honduras, serving as its political, cultural, and economic center.
  • E. Santiago de Cuba
    Santiago de Cuba is a major city in southeastern Cuba known for its rich Afro-Cuban cultural heritage, historic role in the Cuban Revolution, and vibrant music and carnival traditions.
  • 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_69a2573508588190b522c2476d91acfe completed Feb. 28, 2026, 2:47 a.m.
NER Named-entity recognition batch_69a25c6d0fa08190810139b14f4851bc completed Feb. 28, 2026, 3:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69a35ea15b9c819086b6569a5d19de86 completed Feb. 28, 2026, 9:31 p.m.
Created at: Feb. 28, 2026, 2:53 a.m.