Triple
T6701112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eastern Bantu |
E152879
|
entity |
| Predicate | hasMemberLanguage |
P7390
|
FINISHED |
| Object |
Kamba
Kamba is a Bantu language spoken primarily by the Akamba people of Kenya, known for its rich oral traditions and regional cultural significance.
|
E617028
|
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: Kamba | Statement: [Eastern Bantu, hasMemberLanguage, Kamba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kamba Context triple: [Eastern Bantu, hasMemberLanguage, Kamba]
-
A.
Kumba
Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
-
B.
Kumba
Kumba is a major town in southwestern Cameroon known as a commercial hub and cultural crossroads where languages like Cameroonian Pidgin English are widely used.
-
C.
Chambeali
Chambeali is an Indo-Aryan language spoken primarily in the Chamba region of Himachal Pradesh in northern India.
-
D.
Chambo
Chambo is a small town in central Ecuador known for its agricultural activities and proximity to the Andean highlands.
-
E.
Itumbiara
Itumbiara is a municipality in the Brazilian state of Goiás, known for its strategic location on the Paranaíba River and its role as a regional economic and transportation hub.
- 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: Kamba Triple: [Eastern Bantu, hasMemberLanguage, Kamba]
Generated description
Kamba is a Bantu language spoken primarily by the Akamba people of Kenya, known for its rich oral traditions and regional cultural significance.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kamba Target entity description: Kamba is a Bantu language spoken primarily by the Akamba people of Kenya, known for its rich oral traditions and regional cultural significance.
-
A.
Kumba
Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
-
B.
Kumba
Kumba is a major town in southwestern Cameroon known as a commercial hub and cultural crossroads where languages like Cameroonian Pidgin English are widely used.
-
C.
Chambeali
Chambeali is an Indo-Aryan language spoken primarily in the Chamba region of Himachal Pradesh in northern India.
-
D.
Chambo
Chambo is a small town in central Ecuador known for its agricultural activities and proximity to the Andean highlands.
-
E.
Itumbiara
Itumbiara is a municipality in the Brazilian state of Goiás, known for its strategic location on the Paranaíba River and its role as a regional economic and transportation hub.
- 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_69c68807adbc8190b8632df42b39eda0 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d0e4a1848190997520ddd7808cc6 |
completed | March 27, 2026, 6:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7129315c08190a9b72b8119c71e20 |
completed | March 27, 2026, 11:28 p.m. |
| NEDg | Description generation | batch_69c7135f643481908b325739af0c6611 |
completed | March 27, 2026, 11:31 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c713f829e08190973571df498acbb5 |
completed | March 27, 2026, 11:34 p.m. |
Created at: March 27, 2026, 2:05 p.m.