Triple

T1111800
Position Surface form Disambiguated ID Type / Status
Subject Western Nigeria E11011 entity
Predicate borders P224 FINISHED
Object Benin E29788 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: Benin | Statement: [Western Nigeria, borders, Benin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Benin
Context triple: [Western Nigeria, borders, Benin]
  • A. Benin chosen
    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.
  • B. Togo
    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. Côte d'Ivoire
    Côte d'Ivoire is a West African country on the Gulf of Guinea known for its cocoa production, diverse cultures, and economic prominence in the region.
  • 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_69a493252a648190ac48f8742474a5e8 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a4bb7742788190b320aec99e76ca41 completed March 1, 2026, 10:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69b030cd78548190a07264d42e18906b completed March 10, 2026, 2:55 p.m.
Created at: March 1, 2026, 7:43 p.m.