Triple

T10922395
Position Surface form Disambiguated ID Type / Status
Subject Mombasa County E257979 entity
Predicate hasUrbanArea P316 FINISHED
Object Mombasa metropolitan area E47207 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: Mombasa metropolitan area | Statement: [Mombasa County, hasUrbanArea, Mombasa metropolitan area]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mombasa metropolitan area
Context triple: [Mombasa County, hasUrbanArea, Mombasa metropolitan area]
  • A. Mombasa chosen
    Mombasa is a major coastal city in Kenya known as a key regional port and historic trading hub on the Indian Ocean.
  • B. Nairobi Metropolitan Region
    Nairobi Metropolitan Region is the expansive urban and economic area centered on Kenya’s capital, Nairobi, encompassing the city and its surrounding counties and towns.
  • C. Nairobi–Dar es Salaam
    Nairobi–Dar es Salaam is a key regional air route linking Kenya’s capital Nairobi with Tanzania’s largest city and commercial hub, Dar es Salaam.
  • D. Nairobi
    Nairobi is the capital and largest city of Kenya, serving as a major political, economic, and cultural hub in East Africa.
  • E. Nairobi
    Nairobi is a fan-favorite character from the Spanish series "Money Heist," known for her sharp leadership, optimism, and expertise in overseeing the gang’s money-printing operations.
  • 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_69d6aa864ed88190818280ab6791d065 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7708d1fb88190bb33b72d4330ce11 completed April 9, 2026, 9:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69e34455ea4c8190b6f2433f3f745b76 completed April 18, 2026, 8:44 a.m.
Created at: April 8, 2026, 9:22 p.m.