Triple
T1187835
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southern Bantu |
E25286
|
entity |
| Predicate | hasMemberLanguage |
P7390
|
FINISHED |
| Object | Sena |
E79208
|
NE FINISHED |
How this triple was built (2 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: Sena | Statement: [Southern Bantu, hasMemberLanguage, Sena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sena Context triple: [Southern Bantu, hasMemberLanguage, Sena]
-
A.
Sena
chosen
Sena is a Bantu language spoken primarily along the Zambezi River region of central Mozambique and parts of neighboring countries.
-
B.
Beni
Beni is a sparsely populated, largely Amazonian department in northeastern Bolivia known for its tropical lowlands, cattle ranching, and rich indigenous cultures.
-
C.
Beni
Beni is a city in the eastern Democratic Republic of the Congo that became internationally known as a major hotspot of conflict and public health crises, including serving as the epicenter of the 2018–2020 Kivu Ebola epidemic.
-
D.
Senaki
Senaki is a town in western Georgia that serves as an important local administrative and transportation center in the Samegrelo region.
-
E.
Ihnasya
Ihnasya is a city in Egypt known for its location within the Beni Suef Governorate along the Nile Valley.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a49427d98881908646d6c63b8cea1e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd568cf481908d10cf19a3ce28f3 |
completed | March 1, 2026, 10:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac764a5f508190a54c8f01cf0b0d11 |
completed | March 7, 2026, 7:02 p.m. |
Created at: March 1, 2026, 7:45 p.m.