Triple

T648455
Position Surface form Disambiguated ID Type / Status
Subject Kenya E11292 entity
Predicate borderCountry P224 FINISHED
Object Tanzania E19037 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: Tanzania | Statement: [Kenya, borderCountry, Tanzania]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tanzania
Context triple: [Kenya, borderCountry, Tanzania]
  • A. Tanzania chosen
    Tanzania is an East African nation known for its vast wilderness areas, including the Serengeti National Park and Mount Kilimanjaro, as well as its rich cultural diversity.
  • B. Mozambique
    Mozambique is a southeastern African nation on the Indian Ocean known for its Portuguese colonial heritage, rich cultural diversity, and extensive coastline with important ports and marine resources.
  • C. Malawi
    Malawi is a landlocked country in southeastern Africa known for Lake Malawi, its predominantly agricultural economy, and membership in regional and international organizations including the Commonwealth.
  • D. Zanzibar
    Zanzibar is a historically significant island and port off the coast of East Africa that became a key hub for Indian Ocean trade, particularly in spices, ivory, and slaves.
  • E. Kenya
    Kenya is an East African country known for its diverse wildlife, scenic landscapes from savannas to highlands, and a coastline along the Indian Ocean.
  • 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_69a493266a2881909daf4c40f719dee8 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49f308f34819094ba28cfc786051e completed March 1, 2026, 8:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69acce4b94d08190b7747b0b6d61e3e1 completed March 8, 2026, 1:18 a.m.
Created at: March 1, 2026, 7:36 p.m.