Triple

T8941369
Position Surface form Disambiguated ID Type / Status
Subject Kibera E212907 entity
Predicate partOf P40 FINISHED
Object Nairobi metropolitan area E216466 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 metropolitan area | Statement: [Kibera, partOf, Nairobi metropolitan area]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nairobi metropolitan area
Context triple: [Kibera, partOf, Nairobi metropolitan area]
  • A. Nairobi Metropolitan Region chosen
    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.
  • B. Nairobi
    Nairobi is the capital and largest city of Kenya, serving as a major political, economic, and cultural hub in East Africa.
  • C. 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.
  • D. Lipa City
    Lipa City is a highly urbanized city in Batangas, Philippines, known as a commercial, educational, and religious center in the Calabarzon region.
  • E. Central Business District of Nairobi
    The Central Business District of Nairobi is the city’s primary commercial and administrative hub, characterized by high-rise offices, major banks, government institutions, and dense urban activity.
  • 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_69ca839694c88190b324ffeb43d23b08 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc66b9c14c8190b80c3df0cdba2747 completed April 1, 2026, 12:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69d01732bf408190b64ce7687d91a502 completed April 3, 2026, 7:38 p.m.
Created at: March 30, 2026, 6:58 p.m.