Triple

T4032276
Position Surface form Disambiguated ID Type / Status
Subject International Institute of Tropical Agriculture E83740 entity
Predicate hasResearchStationIn P11730 FINISHED
Object Dar es Salaam E108100 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: Dar es Salaam | Statement: [International Institute of Tropical Agriculture, hasResearchStationIn, Dar es Salaam]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dar es Salaam
Context triple: [International Institute of Tropical Agriculture, hasResearchStationIn, Dar es Salaam]
  • A. Dar es Salaam chosen
    Dar es Salaam is a major coastal metropolis on the Indian Ocean and the principal economic and commercial hub of Tanzania.
  • B. Dodoma
    Dodoma is the political and administrative capital city of Tanzania, located in the country’s central region.
  • C. Zanzibar City
    Zanzibar City is the historic and administrative capital of Zanzibar, Tanzania, renowned for its UNESCO-listed Stone Town and rich Swahili, Arab, and colonial heritage.
  • D. Mombasa
    Mombasa is a major coastal city in Kenya known as a key regional port and historic trading hub on the Indian Ocean.
  • E. Arusha, Tanzania
    Arusha, Tanzania is a major city in northern Tanzania known as a diplomatic hub and gateway to popular safari destinations and Mount Kilimanjaro.
  • 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_69aed92e29ac819080f7a98b594fec05 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af01994b0c8190b34af36acadad5c6 completed March 9, 2026, 5:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5563b6d8c8190862597ea39b56de1 completed March 14, 2026, 12:36 p.m.
Created at: March 9, 2026, 3:36 p.m.