Triple

T8540982
Position Surface form Disambiguated ID Type / Status
Subject Terminal 1A E202192 entity
Predicate locatedInCountry P40 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: [Terminal 1A, locatedInCountry, Kenya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kenya
Context triple: [Terminal 1A, locatedInCountry, 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. Nyeri, Kenya
    Nyeri, Kenya is a town in central Kenya near the Aberdare Range, known for its colonial-era history, agricultural economy, and as the final resting place of Scouting founder Robert Baden-Powell.
  • C. Uganda
    Uganda is a landlocked country in East Africa known for its diverse landscapes, abundant wildlife, and location along the equator.
  • D. 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.
  • E. 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.
  • 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_69ca832461e88190a654c5e44e233aa8 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe6e10bc081909a7210c577b807fb completed March 31, 2026, 3:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce6d9d06e48190a5c0cfa9779fc07c completed April 2, 2026, 1:22 p.m.
Created at: March 30, 2026, 6:18 p.m.