Triple

T101575
Position Surface form Disambiguated ID Type / Status
Subject Nigeria E2050 entity
Predicate sharesBorderWith P224 FINISHED
Object Niger E22368 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: Niger | Statement: [Nigeria, sharesBorderWith, Niger]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Niger
Context triple: [Nigeria, sharesBorderWith, Niger]
  • A. Niger chosen
    Niger is a landlocked West African country in the Sahel region, known for its vast desert landscapes, uranium resources, and predominantly rural population.
  • B. 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.
  • C. Cameroon
    Cameroon is a Central African country known for its cultural and linguistic diversity, varied geography from coast to rainforest and savanna, and a mixed French-English colonial heritage.
  • D. Sudan
    Sudan is a large Northeast African country along the Nile River, known for its diverse cultures, ancient Nubian history, and a modern history marked by civil conflict and the secession of South Sudan.
  • 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_69a24e0a5b7c81908d52da08c60dabc4 completed Feb. 28, 2026, 2:08 a.m.
NER Named-entity recognition batch_69a256a8b6d0819083838a9708759407 completed Feb. 28, 2026, 2:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69a36473a3d88190b20052b2c0d272a2 completed Feb. 28, 2026, 9:56 p.m.
Created at: Feb. 28, 2026, 2:12 a.m.