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.