Triple

T13483678
Position Surface form Disambiguated ID Type / Status
Subject South-South Nigeria E318437 entity
Predicate majorCity P316 FINISHED
Object Calabar E87286 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: Calabar | Statement: [South-South Nigeria, majorCity, Calabar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Calabar
Context triple: [South-South Nigeria, majorCity, Calabar]
  • A. Calabar chosen
    Calabar is a historic port city in southeastern Nigeria known for its role in the transatlantic slave trade and its vibrant cultural festivals.
  • B. Ebolowa
    Ebolowa is a city in southern Cameroon that serves as an administrative and commercial center for the surrounding agricultural region.
  • C. Ewondo
    Ewondo is a Bantu language spoken primarily by the Ewondo people in central Cameroon, including in and around the capital city, Yaoundé.
  • D. Nkawkaw
    Nkawkaw is a major commercial town and transport hub in southern Ghana, located along the Accra–Kumasi highway at the base of the Kwahu Plateau.
  • E. Benina
    Benina is a town in eastern Libya that serves as the main gateway to the nearby city of Benghazi through its international airport.
  • 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_69d806b6bfec819089222715b2e86c8e completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dbaf3868ec8190a6a1803018d4f2d8 completed April 12, 2026, 2:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7463715dc8190a70a17b3ea661006 completed May 3, 2026, 12:57 p.m.
Created at: April 9, 2026, 9:42 p.m.