Triple

T16364211
Position Surface form Disambiguated ID Type / Status
Subject National Museum Calabar E397391 entity
Predicate locatedIn P40 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: [National Museum Calabar, locatedIn, Calabar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Calabar
Context triple: [National Museum Calabar, locatedIn, 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_69d87f2778dc8190aa95c7572db127e6 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e2ff3bb5e481909669164a37d76b19 completed April 18, 2026, 3:49 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00679a900c8190aeb7a273943bf553 completed May 10, 2026, 11:10 a.m.
Created at: April 10, 2026, 5:08 a.m.