Triple

T7887635
Position Surface form Disambiguated ID Type / Status
Subject Tanzania National Parks system E183142 entity
Predicate hasHeadquartersIn P62 FINISHED
Object Arusha E45051 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: Arusha | Statement: [Tanzania National Parks system, hasHeadquartersIn, Arusha]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Arusha
Context triple: [Tanzania National Parks system, hasHeadquartersIn, Arusha]
  • A. Arusha, Tanzania chosen
    Arusha, Tanzania is a major city in northern Tanzania known as a diplomatic hub and gateway to popular safari destinations and Mount Kilimanjaro.
  • B. Dodoma
    Dodoma is the political and administrative capital city of Tanzania, located in the country’s central region.
  • C. Dar es Salaam
    Dar es Salaam is a major coastal metropolis on the Indian Ocean and the principal economic and commercial hub of Tanzania.
  • D. Arusha Region
    Arusha Region is an administrative region in northern Tanzania known for its tourism hub city of Arusha and proximity to major national parks and Mount Kilimanjaro.
  • E. Oshakati
    Oshakati is a major northern Namibian town that serves as an important commercial and administrative hub.
  • 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_69ca828af6e48190a06ee7010d8f0e64 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb39d97460819089e37169813af5c2 completed March 31, 2026, 3:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5b98f9b88190b25b5c23a9ae9ced completed March 31, 2026, 5:28 a.m.
Created at: March 30, 2026, 4:59 p.m.