Triple
T16351317
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nakh languages |
E397067
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Bats language
Bats language is a highly endangered Nakh language spoken by the Batsbi people in northeastern Georgia.
|
E1208651
|
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: Bats language | Statement: [Nakh languages, hasPart, Bats language]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bats language Context triple: [Nakh languages, hasPart, Bats language]
-
A.
Batuley language
The Batuley language is an Austronesian language spoken by a small community in Indonesia’s Aru Islands.
-
B.
Limba
The Limba are one of the largest and oldest indigenous ethnic groups in Sierra Leone, known for their distinct language, cultural traditions, and historical role in the country’s northern regions.
-
C.
Simbo language
The Simbo language is an Oceanic language spoken on Simbo Island in the Western Province of the Solomon Islands.
-
D.
Fur language
The Fur language is an Eastern Sudanic language spoken primarily by the Fur people of western Sudan, especially in the Darfur region.
-
E.
Bicep language
Bicep language is a domain-specific, declarative language developed by Microsoft for authoring Azure infrastructure-as-code templates more simply and reliably than traditional JSON-based ARM templates.
- 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: Bats language Triple: [Nakh languages, hasPart, Bats language]
Generated description
Bats language is a highly endangered Nakh language spoken by the Batsbi people in northeastern Georgia.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bats language Target entity description: Bats language is a highly endangered Nakh language spoken by the Batsbi people in northeastern Georgia.
-
A.
Batuley language
The Batuley language is an Austronesian language spoken by a small community in Indonesia’s Aru Islands.
-
B.
Limba
The Limba are one of the largest and oldest indigenous ethnic groups in Sierra Leone, known for their distinct language, cultural traditions, and historical role in the country’s northern regions.
-
C.
Simbo language
The Simbo language is an Oceanic language spoken on Simbo Island in the Western Province of the Solomon Islands.
-
D.
Fur language
The Fur language is an Eastern Sudanic language spoken primarily by the Fur people of western Sudan, especially in the Darfur region.
-
E.
Bicep language
Bicep language is a domain-specific, declarative language developed by Microsoft for authoring Azure infrastructure-as-code templates more simply and reliably than traditional JSON-based ARM templates.
- 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_69d87f26864c819088365ca381a003c2 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2facb37d0819093fe45446f1e79c1 |
completed | April 18, 2026, 3:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a002db6375c81908c64dbe2bc987b1a |
completed | May 10, 2026, 7:03 a.m. |
| NEDg | Description generation | batch_6a00304d7c888190a018d865eb51f0a0 |
completed | May 10, 2026, 7:14 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00309ceba88190a982b439a1e72e78 |
completed | May 10, 2026, 7:15 a.m. |
Created at: April 10, 2026, 5:07 a.m.