Triple

T14786294
Position Surface form Disambiguated ID Type / Status
Subject Sebat Bet Gurage E347533 entity
Predicate hasDialect P4251 FINISHED
Object Muher
Muher is a Gurage language variety spoken in Ethiopia, known as one of the dialects of the Sebat Bet Gurage cluster within the Ethiosemitic language family.
E1119786 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: Muher | Statement: [Sebat Bet Gurage, hasDialect, Muher]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Muher
Context triple: [Sebat Bet Gurage, hasDialect, Muher]
  • A. Muhu
    Muhu is a large Estonian island in the Baltic Sea known for its traditional villages, distinctive folk culture, and role as a gateway between the mainland and Saaremaa.
  • B. Mahur
    Mahur is a small town in the Dima Hasao district of Assam, India, known as a local commercial and transport hub in the region’s hilly terrain.
  • C. Mahur
    Mahur is a small settlement located in the Lihir Islands of Papua New Guinea, known for its remote island community and proximity to major gold mining operations.
  • D. Mugatu
    Mugatu is the flamboyant, villainous fashion designer portrayed by Will Ferrell in the comedy film "Zoolander."
  • E. Moxhe
    Moxhe is a village in the municipality of Hannut in the province of Liège, Belgium.
  • 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: Muher
Triple: [Sebat Bet Gurage, hasDialect, Muher]
Generated description
Muher is a Gurage language variety spoken in Ethiopia, known as one of the dialects of the Sebat Bet Gurage cluster within the Ethiosemitic language family.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Muher
Target entity description: Muher is a Gurage language variety spoken in Ethiopia, known as one of the dialects of the Sebat Bet Gurage cluster within the Ethiosemitic language family.
  • A. Muhu
    Muhu is a large Estonian island in the Baltic Sea known for its traditional villages, distinctive folk culture, and role as a gateway between the mainland and Saaremaa.
  • B. Mahur
    Mahur is a small town in the Dima Hasao district of Assam, India, known as a local commercial and transport hub in the region’s hilly terrain.
  • C. Mahur
    Mahur is a small settlement located in the Lihir Islands of Papua New Guinea, known for its remote island community and proximity to major gold mining operations.
  • D. Mugatu
    Mugatu is the flamboyant, villainous fashion designer portrayed by Will Ferrell in the comedy film "Zoolander."
  • E. Moxhe
    Moxhe is a village in the municipality of Hannut in the province of Liège, Belgium.
  • 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_69d822e9b9e08190bedcc31a163fda82 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69decaa083e481908336d58d026eec32 completed April 14, 2026, 11:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe24b9e8a08190bc736ac207b77324 completed May 8, 2026, 6 p.m.
NEDg Description generation batch_69fe266a05308190b9f6adba3e635e2f completed May 8, 2026, 6:07 p.m.
NED2 Entity disambiguation (via description) batch_69fe26f8fb7881909892c381b16fc80a completed May 8, 2026, 6:10 p.m.
Created at: April 10, 2026, 1:31 a.m.