Triple

T340787
Position Surface form Disambiguated ID Type / Status
Subject Hausa E6832 entity
Predicate spokenIn P2266 FINISHED
Object Sudan E14363 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: Sudan | Statement: [Hausa, spokenIn, Sudan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sudan
Context triple: [Hausa, spokenIn, Sudan]
  • A. Sudan chosen
    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.
  • B. South Sudan
    South Sudan is a landlocked country in East-Central Africa that gained independence from Sudan in 2011 and has since faced ongoing political instability and humanitarian challenges.
  • C. Dongolawi
    Dongolawi is a Nubian language spoken primarily along the Nile in northern Sudan, known for its role in preserving the cultural and linguistic heritage of the Nubian people.
  • D. Nubia
    Nubia is a historic region along the Nile in southern Egypt and northern Sudan, renowned for its ancient civilizations, archaeological sites, and monumental temples.
  • E. Chad
    Chad is a landlocked country in north-central Africa known for its ethnic and linguistic diversity, vast desert regions, 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_69a2e7951ba08190960e90823b5078f3 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2eae611f88190955fbebe2b01835b completed Feb. 28, 2026, 1:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5103417008190ab3755dbba5f7622 completed March 2, 2026, 4:21 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.