Triple
T7792408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Banda-Linda language |
E180211
|
entity |
| Predicate | hasAlternativeName |
P39
|
FINISHED |
| Object |
Banda Linda
Banda Linda is a Central Sudanic language spoken by the Banda people in parts of the Central African Republic and neighboring regions.
|
E693894
|
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: Banda Linda | Statement: [Banda-Linda language, hasAlternativeName, Banda Linda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Banda Linda Context triple: [Banda-Linda language, hasAlternativeName, Banda Linda]
-
A.
Combarbalá
Combarbalá is a small Chilean town and municipality in the Coquimbo Region, known for its semi-arid landscapes, goat farming, and distinctive combarbalite stone crafts.
-
B.
Lapa
Lapa is a historic and bohemian neighborhood in Rio de Janeiro, Brazil, famous for its vibrant nightlife, samba clubs, and iconic aqueduct arches.
-
C.
Trancoso
Trancoso is a historic Portuguese town in the Centro Region, known for its medieval walls, castle, and well-preserved old quarter.
-
D.
Pirassununga
Pirassununga is a municipality in the state of São Paulo, Brazil, known for its agricultural activities and as a site of a major University of São Paulo campus.
-
E.
Caxangá
Caxangá is a neighborhood and important urban area within the city of Recife, Brazil.
- 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: Banda Linda Triple: [Banda-Linda language, hasAlternativeName, Banda Linda]
Generated description
Banda Linda is a Central Sudanic language spoken by the Banda people in parts of the Central African Republic and neighboring regions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Banda Linda Target entity description: Banda Linda is a Central Sudanic language spoken by the Banda people in parts of the Central African Republic and neighboring regions.
-
A.
Combarbalá
Combarbalá is a small Chilean town and municipality in the Coquimbo Region, known for its semi-arid landscapes, goat farming, and distinctive combarbalite stone crafts.
-
B.
Lapa
Lapa is a historic and bohemian neighborhood in Rio de Janeiro, Brazil, famous for its vibrant nightlife, samba clubs, and iconic aqueduct arches.
-
C.
Trancoso
Trancoso is a historic Portuguese town in the Centro Region, known for its medieval walls, castle, and well-preserved old quarter.
-
D.
Pirassununga
Pirassununga is a municipality in the state of São Paulo, Brazil, known for its agricultural activities and as a site of a major University of São Paulo campus.
-
E.
Caxangá
Caxangá is a neighborhood and important urban area within the city of Recife, Brazil.
- 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_69ca827d22208190b4dc5aa680edcf5d |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cae938714c8190b89917e6ded004da |
completed | March 30, 2026, 9:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb13cdb4288190ae3cfe1ee4e3e496 |
completed | March 31, 2026, 12:22 a.m. |
| NEDg | Description generation | batch_69cb1636b0d48190a57c2d3a7b3b41ed |
completed | March 31, 2026, 12:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cb1a29d2988190bb64aada0d2ef463 |
completed | March 31, 2026, 12:49 a.m. |
Created at: March 30, 2026, 4:30 p.m.