Triple

T17931793
Position Surface form Disambiguated ID Type / Status
Subject Force Publique E448350 entity
Predicate garrisonLocation P40 FINISHED
Object Boma 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: Boma | Statement: [Force Publique, garrisonLocation, Boma]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Boma
Context triple: [Force Publique, garrisonLocation, Boma]
  • A. Boma
    Boma is a buffet-style African-inspired restaurant at Disney’s Animal Kingdom Lodge known for its diverse flavors and vibrant, marketplace-like atmosphere.
  • B. Boma chosen
    Boma is a historic port city on the Congo River in present-day Democratic Republic of the Congo that served as a major colonial administrative and trading center.
  • C. Boma Ng’ombe
    Boma Ng’ombe is a town in northern Tanzania that serves as an administrative and commercial hub in the Kilimanjaro Region.
  • D. Benina
    Benina is a town in eastern Libya that serves as the main gateway to the nearby city of Benghazi through its international airport.
  • E. Dutsin-Ma
    Dutsin-Ma is a town in northern Nigeria known for hosting the Federal University Dutsin-Ma and serving as an important local commercial and educational center.
  • 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_69d8b9f79d14819095540856928f0e25 completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e4a552bb848190871251474cc208d5 completed April 19, 2026, 9:50 a.m.
Created at: April 10, 2026, 10:20 a.m.