Triple

T8853247
Position Surface form Disambiguated ID Type / Status
Subject Wele-Nzas E210688 entity
Predicate containsCity P294 FINISHED
Object Mongomo E207844 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: Mongomo | Statement: [Wele-Nzas, containsCity, Mongomo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mongomo
Context triple: [Wele-Nzas, containsCity, Mongomo]
  • A. Mongomo chosen
    Mongomo is a town in mainland Equatorial Guinea that serves as an important political center and the home region of much of the country’s ruling elite.
  • B. Dongo
    Dongo is a small town on the northwestern shore of Lake Como in Lombardy, Italy, known for its role in the capture of Benito Mussolini at the end of World War II.
  • C. Wamba
    Wamba was a 7th-century king of the Visigoths in Hispania, known for his military campaigns and efforts to strengthen royal authority.
  • D. Wamba
    Wamba is a town and administrative local government area in Nasarawa State, central Nigeria, known for its diverse ethnic communities and agricultural activities.
  • E. Machar
    Machar is a small rural township in Ontario, Canada, known for its forests, lakes, and low-density residential and agricultural character.
  • 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_69ca838a424c8190b1ecac115c2927e7 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc60c55e348190957b3bbb7397e380 completed April 1, 2026, 12:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfab8911c8819083f5caa318071720 completed April 3, 2026, 11:59 a.m.
Created at: March 30, 2026, 6:49 p.m.