Triple

T5700530
Position Surface form Disambiguated ID Type / Status
Subject Governing Council of UN-Habitat E125646 entity
Predicate location P40 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: [Governing Council of UN-Habitat, location, Nairobi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nairobi
Context triple: [Governing Council of UN-Habitat, location, 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. 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.
  • C. Kinondoni
    Kinondoni is a major urban district within Dar es Salaam, Tanzania, known for its dense population, commercial activity, and diverse residential neighborhoods.
  • D. Mombasa
    Mombasa is a major coastal city in Kenya known as a key regional port and historic trading hub on the Indian Ocean.
  • E. 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.
  • 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_69c0082c96988190b3a6a201edce472a completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c024540afc8190aee3760f71ea39c2 completed March 22, 2026, 5:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c05a5fe4fc8190944a63a29da0fe3c completed March 22, 2026, 9:08 p.m.
Created at: March 22, 2026, 3:45 p.m.