Triple

T632012
Position Surface form Disambiguated ID Type / Status
Subject Islamic world E15944 entity
Predicate hasCulturalCenter P2412 FINISHED
Object Abuja E9148 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: Abuja | Statement: [Islamic world, hasCulturalCenter, Abuja]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Abuja
Context triple: [Islamic world, hasCulturalCenter, Abuja]
  • A. Abuja chosen
    Abuja is a planned city in central Nigeria that serves as the country’s political and administrative center.
  • B. Lagos
    Lagos is a major coastal megacity in southwestern Nigeria, known as the country’s economic hub and one of Africa’s most populous and vibrant urban centers.
  • C. Lagos
    Lagos is a historic coastal city in Portugal’s Algarve region, known for its scenic beaches, dramatic cliffs, and well-preserved old town.
  • D. Ibadan
    Ibadan is one of the largest and most populous cities in southwestern Nigeria, historically significant as a major Yoruba cultural and economic center.
  • E. Bauchi
    Bauchi is a prominent city in northeastern Nigeria that serves as the capital of Bauchi State and a key commercial and administrative center in the region.
  • 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_69a4935c131c8190a5378c6bf101e8cc completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a49ec2a4c08190bc5c6ce8a10b0967 completed March 1, 2026, 8:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69a56c4d84d8819095afbf0ee9c7bd82 completed March 2, 2026, 10:54 a.m.
Created at: March 1, 2026, 7:35 p.m.