Triple

T5037681
Position Surface form Disambiguated ID Type / Status
Subject Lake Chad E113465 entity
Predicate countryBorder 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: [Lake Chad, countryBorder, Niger]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Niger
Context triple: [Lake Chad, countryBorder, 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. Guinea
    Guinea is a West African country on the Atlantic coast known for its rich mineral resources, diverse ethnic groups, and role as a major producer of bauxite.
  • C. Mali
    Mali is a landlocked West African country known for its historic trading cities like Timbuktu, rich Sahelian culture, and significant role in the ancient Mali Empire.
  • D. Río de Oro
    Río de Oro was a former Spanish colonial territory in northwest Africa that later became part of the disputed region of Western Sahara.
  • E. Ennedi
    Ennedi is a remote region in northeastern Chad renowned for its dramatic sandstone massifs, rock arches, and prehistoric rock art.
  • 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_69bd44384298819089c49e7c330ec7b8 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd73daaa788190b670f6c328bfa44f completed March 20, 2026, 4:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69bea479f01c8190a84ff4973845eb17 completed March 21, 2026, 2 p.m.
Created at: March 20, 2026, 1:37 p.m.