Triple

T17168332
Position Surface form Disambiguated ID Type / Status
Subject Rutul language E416663 entity
Predicate hasDialect P4251 FINISHED
Object Mukrek dialect
The Mukrek dialect is a regional variety of the Rutul language spoken by Rutul communities in the Caucasus.
 E1253882 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: Mukrek dialect | Statement: [Rutul language, hasDialect, Mukrek dialect]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mukrek dialect
Context triple: [Rutul language, hasDialect, Mukrek dialect]
  • A. Mukhad dialect
    The Mukhad dialect is a regional variety of the Rutul language spoken by Rutul communities in the Caucasus.
  • B. Zanniat dialect
    The Zanniat dialect is a regional variety of the Falam Chin language spoken by the Zanniat people in parts of Chin State, Myanmar.
  • C. Kebkabiya dialect
    The Kebkabiya dialect is a regional variety of the Foor language spoken around the Kebkabiya area, distinguished by its local phonological and lexical features.
  • D. Takbanuaz dialect
    The Takbanuaz dialect is a regional variety of the Bunun language spoken by an indigenous Bunun subgroup in Taiwan.
  • E. Mrass dialect
    The Mrass dialect is a regional variety of the Shor language traditionally spoken by Shor communities along the Mrass River in southwestern Siberia.
  • 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: Mukrek dialect
Triple: [Rutul language, hasDialect, Mukrek dialect]
Generated description
The Mukrek dialect is a regional variety of the Rutul language spoken by Rutul communities in the Caucasus.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mukrek dialect
Target entity description: The Mukrek dialect is a regional variety of the Rutul language spoken by Rutul communities in the Caucasus.
  • A. Mukhad dialect chosen
    The Mukhad dialect is a regional variety of the Rutul language spoken by Rutul communities in the Caucasus.
  • B. Zanniat dialect
    The Zanniat dialect is a regional variety of the Falam Chin language spoken by the Zanniat people in parts of Chin State, Myanmar.
  • C. Kebkabiya dialect
    The Kebkabiya dialect is a regional variety of the Foor language spoken around the Kebkabiya area, distinguished by its local phonological and lexical features.
  • D. Takbanuaz dialect
    The Takbanuaz dialect is a regional variety of the Bunun language spoken by an indigenous Bunun subgroup in Taiwan.
  • E. Mrass dialect
    The Mrass dialect is a regional variety of the Shor language traditionally spoken by Shor communities along the Mrass River in southwestern Siberia.
  • F. None of above.

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_69d886d5f34c8190b24564dfaa63f3fb completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3f9173ee48190bc46622c78479603 completed April 18, 2026, 9:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a015fc83984819098c98b75cf021e3a completed May 11, 2026, 4:49 a.m.
NEDg Description generation batch_6a0160beb4188190b6e9a91f50b6e276 completed May 11, 2026, 4:53 a.m.
NED2 Entity disambiguation (via description) batch_6a016158b1d081909b62cf73e14b3e78 completed May 11, 2026, 4:55 a.m.
Created at: April 10, 2026, 5:37 a.m.