Triple
T4669793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | São Vicente |
E102933
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Bela Vista
Bela Vista is a settlement located on the island and municipality of São Vicente in Cape Verde.
|
E460738
|
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: Bela Vista | Statement: [São Vicente, hasSettlement, Bela Vista]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bela Vista Context triple: [São Vicente, hasSettlement, Bela Vista]
-
A.
Bella Vista
Bella Vista is a historic, culturally vibrant neighborhood in South Philadelphia known for its Italian Market and diverse dining scene.
-
B.
Bella Vista
Bella Vista is a small unincorporated community in Northern California’s Shasta County, known for its rural setting near Redding.
-
C.
Santa Bárbara d'Oeste
Santa Bárbara d'Oeste is a municipality in the interior of Brazil’s state of São Paulo, known for its industrial activity and historical role in receiving North American Confederate immigrants in the 19th century.
-
D.
San Luis
San Luis is a residential and commercial district located in the eastern part of Lima, Peru.
-
E.
San Luis
San Luis is a landlocked agricultural municipality in the province of Pampanga in the Philippines, known for its rice fields and rural communities.
- 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: Bela Vista Triple: [São Vicente, hasSettlement, Bela Vista]
Generated description
Bela Vista is a settlement located on the island and municipality of São Vicente in Cape Verde.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bela Vista Target entity description: Bela Vista is a settlement located on the island and municipality of São Vicente in Cape Verde.
-
A.
Bella Vista
Bella Vista is a historic, culturally vibrant neighborhood in South Philadelphia known for its Italian Market and diverse dining scene.
-
B.
Bella Vista
Bella Vista is a small unincorporated community in Northern California’s Shasta County, known for its rural setting near Redding.
-
C.
Santa Bárbara d'Oeste
Santa Bárbara d'Oeste is a municipality in the interior of Brazil’s state of São Paulo, known for its industrial activity and historical role in receiving North American Confederate immigrants in the 19th century.
-
D.
San Luis
San Luis is a residential and commercial district located in the eastern part of Lima, Peru.
-
E.
San Luis
San Luis is a landlocked agricultural municipality in the province of Pampanga in the Philippines, known for its rice fields and rural communities.
- 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_69bd43d9cba4819086c1ab1c2d9d2133 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd634ef5608190925663e988e3585b |
completed | March 20, 2026, 3:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be0390c238819089fb54648dfe1e64 |
completed | March 21, 2026, 2:33 a.m. |
| NEDg | Description generation | batch_69be0542daf08190b792855c8129ac50 |
completed | March 21, 2026, 2:41 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be05c1dcd48190a08a5748e86a5ac8 |
completed | March 21, 2026, 2:43 a.m. |
Created at: March 20, 2026, 1:15 p.m.