Triple

T11012491
Position Surface form Disambiguated ID Type / Status
Subject Kam language E260275 entity
Predicate hasDialect P4251 FINISHED
Object Southern Kam
Southern Kam is a major dialect of the Kam language spoken by the Kam (Dong) people in parts of southern China.
E899584 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: Southern Kam | Statement: [Kam language, hasDialect, Southern Kam]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Southern Kam
Context triple: [Kam language, hasDialect, Southern Kam]
  • A. Southern Tat
    Southern Tat is a regional variety of the Tat language spoken by Tat communities in parts of the South Caucasus.
  • B. Southern Lau
    Southern Lau is a dialectal variety of the Lau language spoken on Malaita in the Solomon Islands.
  • C. Southern Uma
    Southern Uma is a regional dialect of the Uma language spoken by a subset of Uma-speaking communities, distinguished by its own phonological and lexical features.
  • D. South Central Coast
    The South Central Coast is a coastal region of Vietnam known for its long sandy beaches, fishing communities, and growing tourism hubs such as Nha Trang and Quy Nhon.
  • E. Tây Nguyên
    Tây Nguyên is a mountainous plateau region in central Vietnam known for its diverse ethnic minority communities, coffee production, and extensive forests.
  • 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: Southern Kam
Triple: [Kam language, hasDialect, Southern Kam]
Generated description
Southern Kam is a major dialect of the Kam language spoken by the Kam (Dong) people in parts of southern China.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Southern Kam
Target entity description: Southern Kam is a major dialect of the Kam language spoken by the Kam (Dong) people in parts of southern China.
  • A. Southern Tat
    Southern Tat is a regional variety of the Tat language spoken by Tat communities in parts of the South Caucasus.
  • B. Southern Lau
    Southern Lau is a dialectal variety of the Lau language spoken on Malaita in the Solomon Islands.
  • C. Southern Uma
    Southern Uma is a regional dialect of the Uma language spoken by a subset of Uma-speaking communities, distinguished by its own phonological and lexical features.
  • D. South Central Coast
    The South Central Coast is a coastal region of Vietnam known for its long sandy beaches, fishing communities, and growing tourism hubs such as Nha Trang and Quy Nhon.
  • E. Tây Nguyên
    Tây Nguyên is a mountainous plateau region in central Vietnam known for its diverse ethnic minority communities, coffee production, and extensive forests.
  • 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_69d6aa9687448190b28d353b1b6a610e completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7978a57a881909b4ceae0ebe21b78 completed April 9, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69e374ac78348190a8c0a5a7a736b24b completed April 18, 2026, 12:10 p.m.
NEDg Description generation batch_69e378df767c819099d0bfdf35eaf5f3 completed April 18, 2026, 12:28 p.m.
NED2 Entity disambiguation (via description) batch_69e37bf526108190b5fc22569fe6be54 completed April 18, 2026, 12:41 p.m.
Created at: April 8, 2026, 9:25 p.m.