Triple

T2446324
Position Surface form Disambiguated ID Type / Status
Subject Ewe E53601 entity
Predicate region P40 FINISHED
Object Volta Region E133744 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: Volta Region | Statement: [Ewe, region, Volta Region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Volta Region
Context triple: [Ewe, region, Volta Region]
  • A. Volta Region chosen
    The Volta Region is an eastern administrative region of Ghana known for its diverse Ewe culture, lush landscapes, and attractions such as Lake Volta and Wli Waterfalls.
  • B. Geita Region
    Geita Region is an administrative region in northwestern Tanzania, known for its significant gold mining activities and proximity to Lake Victoria.
  • C. Karas Region
    Karas Region is the southernmost administrative region of Namibia, known for its arid landscapes, desert scenery, and coastal towns along the Atlantic Ocean.
  • D. Tajima region
    The Tajima region is a northern area of Hyōgo Prefecture in Japan, known for its rural landscapes, hot springs, and the origin of the famed Tajima-gyu cattle used for Kobe beef.
  • E. Taunus region
    The Taunus region is a low mountain range in Hesse, Germany, known for its forested hills, spa towns, and historical castles overlooking the Rhine-Main area.
  • 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_69ab495d227c8190b26ae6548eeb1019 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abca25a84c8190859bf51000beffec completed March 7, 2026, 6:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69aef0bd7a088190b635a8bac233c5cd completed March 9, 2026, 4:09 p.m.
Created at: March 6, 2026, 9:43 p.m.