Triple

T2334411
Position Surface form Disambiguated ID Type / Status
Subject Khartoum North E44276 entity
Predicate formsUrbanAreaWith P38278 FINISHED
Object Omdurman E64455 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: Omdurman | Statement: [Khartoum North, formsUrbanAreaWith, Omdurman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Omdurman
Context triple: [Khartoum North, formsUrbanAreaWith, Omdurman]
  • A. Omdurman chosen
    Omdurman is a major city in Sudan, historically significant as a cultural and commercial center and effectively forming part of the country’s greater capital area.
  • B. Adigrat
    Adigrat is a major town in northern Ethiopia known as a commercial and administrative center near the Eritrean border.
  • C. Zintan
    Zintan is a town in western Libya known for its role in the Libyan Civil War and for being controlled by powerful local militias.
  • D. Dongola
    Dongola is a historic town in northern Sudan that served as a major political and cultural center of medieval Nubian kingdoms along the Nile.
  • E. Tanta
    Tanta is a major city in northern Egypt that serves as an important commercial and transportation hub in the Nile Delta.
  • 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_69a889132b488190bbb43ad4780ddd92 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abd0d6b0e48190aee9131ca182e52f completed March 7, 2026, 7:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69af1f78cce88190a74f9ab6a540fe83 completed March 9, 2026, 7:28 p.m.
Created at: March 4, 2026, 7:51 p.m.