Triple

T10054873
Position Surface form Disambiguated ID Type / Status
Subject General Secretariat (INTERPOL) E208834 entity
Predicate hasOffice P1268 FINISHED
Object Harare regional bureau E837030 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: Harare regional bureau | Statement: [General Secretariat (INTERPOL), hasOffice, Harare regional bureau]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Harare regional bureau
Context triple: [General Secretariat (INTERPOL), hasOffice, Harare regional bureau]
  • A. Harare regional bureau chosen
    The Harare regional bureau is an INTERPOL regional office that coordinates international police cooperation and crime-fighting efforts among member countries in the Southern African region.
  • B. Harare City Council
    Harare City Council is the municipal authority responsible for local governance, services, and administration in Zimbabwe’s capital city, Harare.
  • C. Harare Province
    Harare Province is the metropolitan province in Zimbabwe that encompasses the capital city, Harare, and its surrounding urban areas.
  • D. Harare
    Harare is the largest city and main economic, political, and cultural center of Zimbabwe.
  • E. Manicaland Province
    Manicaland Province is an eastern region of Zimbabwe known for its mountainous landscapes, rich mineral resources, and proximity to the border with Mozambique.
  • 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_69ca836094408190a36a1ea7e9a86fcd completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cdcfab39408190ac728fe156eed658 completed April 2, 2026, 2:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69d29a49cb208190b56d991a523efbac completed April 5, 2026, 5:22 p.m.
Created at: March 30, 2026, 8:57 p.m.