Triple

T3077314
Position Surface form Disambiguated ID Type / Status
Subject Lagos State E64168 entity
Predicate hasCity P316 FINISHED
Object Yaba E288160 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: Yaba | Statement: [Lagos State, hasCity, Yaba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yaba
Context triple: [Lagos State, hasCity, Yaba]
  • A. Yaba chosen
    Yaba is a bustling commercial and residential district on Lagos Mainland in Nigeria, known for its markets, educational institutions, and growing tech startup scene.
  • B. Ile-Ife
    Ile-Ife is an ancient Yoruba city in southwestern Nigeria revered as the spiritual and cultural cradle of the Yoruba people and renowned for its sophisticated early art and urban civilization.
  • C. Ibadan
    Ibadan is one of the largest and most populous cities in southwestern Nigeria, historically significant as a major Yoruba cultural and economic center.
  • D. Dandora
    Dandora is a residential and industrial area in Nairobi, Kenya, best known for hosting one of Africa’s largest open-air garbage dumps.
  • E. Lekki
    Lekki is a rapidly developing coastal city and affluent residential and commercial hub in Lagos State, Nigeria.
  • 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_69ad857a8aec8190bfdfd9c14554ac5a completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada1a6f6148190ae5cd6e45eda9006 completed March 8, 2026, 4:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1f88d32a08190b4e18da4b26b534c completed March 11, 2026, 11:19 p.m.
Created at: March 8, 2026, 3:02 p.m.