Triple

T19075249
Position Surface form Disambiguated ID Type / Status
Subject UCCI E466885 entity
Predicate hasMember P10 FINISHED
Object Malabo NE NERFINISHED

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: Malabo | Statement: [UCCI, hasMember, Malabo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Malabo
Context triple: [UCCI, hasMember, Malabo]
  • A. Malabo chosen
    Malabo is the largest city and main economic and administrative center of Equatorial Guinea, located on the northern coast of Bioko Island in the Gulf of Guinea.
  • B. Luanda
    Luanda is the capital and largest city of Angola, a major Atlantic port and economic hub with a history shaped by Portuguese colonial rule and the transatlantic slave trade.
  • C. Ouaga
    Ouaga is the commonly used short name for Ouagadougou, the capital and largest city of Burkina Faso.
  • D. Brazzaville
    Brazzaville is the capital and largest city of the Republic of the Congo, located on the Congo River directly across from Kinshasa in Central Africa.
  • E. Pointe-Noire
    Pointe-Noire is a major port city on the Atlantic coast of the Republic of the Congo and one of the country’s principal economic and industrial centers.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8dd04f4488190b1121cc53ef2bfd6 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5e2e3c7b08190bf6448ead11ba916 completed April 20, 2026, 8:25 a.m.
Created at: April 10, 2026, 12:04 p.m.