Triple
T16383670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Benga language |
E397868
|
entity |
| Predicate | glottologName |
P6521
|
FINISHED |
| Object |
Benga
Benga is a Bantu language spoken primarily by the Benga people along the coastal regions and islands of Equatorial Guinea and northern Gabon.
|
E1210226
|
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: Benga | Statement: [Benga language, glottologName, Benga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Benga Context triple: [Benga language, glottologName, Benga]
-
A.
Dor Bongo
Dor Bongo is an alternative name for the Bongo language, a Central Sudanic language spoken primarily in South Sudan.
-
B.
Mengoni
Mengoni is an Italian surname most notably associated with figures such as architect Giuseppe Mengoni and contemporary singer Marco Mengoni.
-
C.
Bangala
Bangala is a regional variety of the Bantu language Lingala, spoken primarily in parts of the Democratic Republic of the Congo and neighboring areas.
-
D.
Bongo
Bongo is an animated musical segment from Disney’s 1947 anthology film "Fun and Fancy Free," following the adventures of a circus bear who longs for freedom and love.
-
E.
Bongo
Bongo is a fashion and lifestyle brand known for its trendy, youth-oriented clothing and accessories, historically sold through major U.S. retailers.
- 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: Benga Triple: [Benga language, glottologName, Benga]
Generated description
Benga is a Bantu language spoken primarily by the Benga people along the coastal regions and islands of Equatorial Guinea and northern Gabon.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Benga Target entity description: Benga is a Bantu language spoken primarily by the Benga people along the coastal regions and islands of Equatorial Guinea and northern Gabon.
-
A.
Dor Bongo
Dor Bongo is an alternative name for the Bongo language, a Central Sudanic language spoken primarily in South Sudan.
-
B.
Mengoni
Mengoni is an Italian surname most notably associated with figures such as architect Giuseppe Mengoni and contemporary singer Marco Mengoni.
-
C.
Bangala
Bangala is a regional variety of the Bantu language Lingala, spoken primarily in parts of the Democratic Republic of the Congo and neighboring areas.
-
D.
Bongo
Bongo is an animated musical segment from Disney’s 1947 anthology film "Fun and Fancy Free," following the adventures of a circus bear who longs for freedom and love.
-
E.
Bongo
Bongo is a fashion and lifestyle brand known for its trendy, youth-oriented clothing and accessories, historically sold through major U.S. retailers.
- 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_69d87f2880b48190ae1a9673a3bbef80 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e319de83248190b5d43646fa9b6cda |
completed | April 18, 2026, 5:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00356b00408190beab51a23011be67 |
completed | May 10, 2026, 7:36 a.m. |
| NEDg | Description generation | batch_6a00369391a08190bb5521e2fcc839c6 |
completed | May 10, 2026, 7:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00374326948190ae039bc689054387 |
completed | May 10, 2026, 7:44 a.m. |
Created at: April 10, 2026, 5:08 a.m.