Triple

T15854913
Position Surface form Disambiguated ID Type / Status
Subject WAEMU Commission E384428 entity
Predicate hasMemberState P5029 FINISHED
Object Togo E41698 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: Togo | Statement: [WAEMU Commission, hasMemberState, Togo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Togo
Context triple: [WAEMU Commission, hasMemberState, Togo]
  • A. Togo
    Togo is a 2019 historical adventure drama film that tells the true story of a sled dog and his musher leading a perilous serum run across Alaska.
  • B. Togo chosen
    Togo is a small West African country on the Gulf of Guinea, known for its diverse cultures, coastal capital Lomé, and history as a former French colony.
  • C. Benin
    Benin is a West African country on the Gulf of Guinea known for its historical Kingdom of Dahomey and as a key region in the transatlantic slave trade.
  • D. Burkina Faso
    Burkina Faso is a landlocked West African country known for its diverse cultures, Sahelian landscapes, and capital city, Ouagadougou.
  • E. Gabon
    Gabon is a Central African country on the Atlantic coast, known for its equatorial rainforests, rich biodiversity, and significant oil reserves.
  • 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_69d86da422088190aac39e32e6c68429 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e14caf6ae481909ae1385cb4548612 completed April 16, 2026, 8:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffb03c8cb081908c18c7b2d143c4b4 completed May 9, 2026, 10:07 p.m.
Created at: April 10, 2026, 4:50 a.m.