Triple

T282995
Position Surface form Disambiguated ID Type / Status
Subject G77 E5828 entity
Predicate hasSecretariatLocation P62 FINISHED
Object Nairobi E6371 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: Nairobi | Statement: [G77, hasSecretariatLocation, Nairobi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nairobi
Context triple: [G77, hasSecretariatLocation, Nairobi]
  • A. Nairobi chosen
    Nairobi is the capital and largest city of Kenya, serving as a major political, economic, and cultural hub in East Africa.
  • B. Bulawayo
    Bulawayo is Zimbabwe’s second-largest city and a major industrial, cultural, and transport hub in the southwestern part of the country.
  • C. Khartoum
    Khartoum is the capital and largest city of Sudan, located at the confluence of the Blue and White Nile rivers and serving as a major political, economic, and cultural center in the region.
  • D. Masvingo
    Masvingo is one of Zimbabwe’s oldest urban centers, located in the country’s southeastern region near the Great Zimbabwe ruins.
  • E. Mogadishu
    Mogadishu is the capital and largest city of Somalia, serving as a major political, economic, and cultural center on the Horn of 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_69a25946a7ac8190a78871c210213272 completed Feb. 28, 2026, 2:56 a.m.
NER Named-entity recognition batch_69a260d0dae48190a2ec98d0186fd792 completed Feb. 28, 2026, 3:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69a399a74d448190a4857ce008e64e7e completed March 1, 2026, 1:43 a.m.
Created at: Feb. 28, 2026, 3:02 a.m.