Triple

T4695656
Position Surface form Disambiguated ID Type / Status
Subject United Nations Mission in Sierra Leone E104133 entity
Predicate contributingCountries P51288 FINISHED
Object Kenya E11292 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: Kenya | Statement: [United Nations Mission in Sierra Leone, contributingCountries, Kenya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kenya
Context triple: [United Nations Mission in Sierra Leone, contributingCountries, Kenya]
  • A. Kenya chosen
    Kenya is an East African country known for its diverse wildlife, scenic landscapes from savannas to highlands, and a coastline along the Indian Ocean.
  • B. Uganda
    Uganda is a landlocked country in East Africa known for its diverse landscapes, abundant wildlife, and location along the equator.
  • C. Tanzania
    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.
  • D. Rwanda
    Rwanda is a landlocked East African nation known for its dramatic recovery from the 1994 genocide, rapid economic growth, and strong conservation efforts, particularly for mountain gorillas.
  • E. Central Province, Kenya
    Central Province, Kenya was a former administrative region in central Kenya known for its fertile highlands, tea and coffee production, and predominantly Kikuyu population.
  • 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_69bd43df91f481908e9add1b617b60ef completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd6c3d1cb88190a42919dcbfe2568c completed March 20, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69be03b75e3481908aa27eeaeec490ca completed March 21, 2026, 2:34 a.m.
Created at: March 20, 2026, 1:17 p.m.