Triple

T1337435
Position Surface form Disambiguated ID Type / Status
Subject Shona E28785 entity
Predicate hasDialects P4251 FINISHED
Object Korekore
Korekore is a major dialect of the Shona language spoken primarily in northern Zimbabwe.
 E153636 NE FINISHED

How this triple was built (4 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: Korekore | Statement: [Shona, hasDialects, Korekore]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Korekore
Context triple: [Shona, hasDialects, Korekore]
  • A. Koreiz
    Koreiz is a resort settlement on the southern coast of Crimea, known for its seaside location and historic villas.
  • B. Kawki
    Kawki is an indigenous Andean language closely related to Aymara and spoken by a small number of people in Peru.
  • C. Takanot
    Takanot are rabbinic enactments or decrees established to address communal needs and clarify or safeguard Jewish law within Rabbinic Judaism.
  • D. Kono
    Kono is a Japanese surname most prominently associated with politician Taro Kono, a leading figure in contemporary Japanese politics.
  • E. Rusutsu
    Rusutsu is a major ski and resort area in Japan known for its extensive, high-quality powder snow terrain and year-round outdoor activities.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Korekore
Triple: [Shona, hasDialects, Korekore]
Generated description
Korekore is a major dialect of the Shona language spoken primarily in northern Zimbabwe.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Korekore
Target entity description: Korekore is a major dialect of the Shona language spoken primarily in northern Zimbabwe.
  • A. Koreiz
    Koreiz is a resort settlement on the southern coast of Crimea, known for its seaside location and historic villas.
  • B. Kawki
    Kawki is an indigenous Andean language closely related to Aymara and spoken by a small number of people in Peru.
  • C. Takanot
    Takanot are rabbinic enactments or decrees established to address communal needs and clarify or safeguard Jewish law within Rabbinic Judaism.
  • D. Kono
    Kono is a Japanese surname most prominently associated with politician Taro Kono, a leading figure in contemporary Japanese politics.
  • E. Rusutsu
    Rusutsu is a major ski and resort area in Japan known for its extensive, high-quality powder snow terrain and year-round outdoor activities.
  • F. None of above. chosen

Provenance (5 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_69a498561a508190a3e1bc137c2b866a completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c1edda1c81909a1149b254b0d57e completed March 1, 2026, 10:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69acc62e4c788190824df2a9b81692d7 completed March 8, 2026, 12:43 a.m.
NEDg Description generation batch_69acc6ae66408190bf48fe3150a08116 completed March 8, 2026, 12:45 a.m.
NED2 Entity disambiguation (via description) batch_69acc73b92248190b723cb64046799e3 completed March 8, 2026, 12:47 a.m.
Created at: March 1, 2026, 7:55 p.m.