Triple
T12084489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vale do Rio dos Sinos |
E287770
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Campo Bom
Campo Bom is a municipality in the state of Rio Grande do Sul in southern Brazil, known for its strong footwear industry and integration into the Porto Alegre metropolitan region.
|
E967463
|
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: Campo Bom | Statement: [Vale do Rio dos Sinos, contains, Campo Bom]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Campo Bom Context triple: [Vale do Rio dos Sinos, contains, Campo Bom]
-
A.
Fazenda
Fazenda is a Portuguese and Spanish surname most notably associated with American silent film comedian Louise Fazenda.
-
B.
Campinho
Campinho is a small village in the municipality of Reguengos de Monsaraz in Portugal’s Alentejo region.
-
C.
Cruzcampo
Cruzcampo is a popular Spanish beer brand known for its Andalusian origins and wide distribution throughout Spain.
-
D.
Praça Seca
Praça Seca is a neighborhood in Rio de Janeiro known for its dense urban character and location in the city's West Zone.
-
E.
Lajeado
Lajeado is a city in southern Brazil known for its strong German-Brazilian cultural heritage and traditions.
- 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: Campo Bom Triple: [Vale do Rio dos Sinos, contains, Campo Bom]
Generated description
Campo Bom is a municipality in the state of Rio Grande do Sul in southern Brazil, known for its strong footwear industry and integration into the Porto Alegre metropolitan region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Campo Bom Target entity description: Campo Bom is a municipality in the state of Rio Grande do Sul in southern Brazil, known for its strong footwear industry and integration into the Porto Alegre metropolitan region.
-
A.
Fazenda
Fazenda is a Portuguese and Spanish surname most notably associated with American silent film comedian Louise Fazenda.
-
B.
Campinho
Campinho is a small village in the municipality of Reguengos de Monsaraz in Portugal’s Alentejo region.
-
C.
Cruzcampo
Cruzcampo is a popular Spanish beer brand known for its Andalusian origins and wide distribution throughout Spain.
-
D.
Praça Seca
Praça Seca is a neighborhood in Rio de Janeiro known for its dense urban character and location in the city's West Zone.
-
E.
Lajeado
Lajeado is a city in southern Brazil known for its strong German-Brazilian cultural heritage and traditions.
- 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91513bbb0819084a8bb877e03060c |
completed | April 10, 2026, 3:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f666bf1c819089de1235617e775b |
completed | May 2, 2026, 1:04 p.m. |
| NEDg | Description generation | batch_69f60335285c819089f69472b2e48130 |
completed | May 2, 2026, 1:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f60410ce0481908b2deb7522a3ec00 |
completed | May 2, 2026, 2:02 p.m. |
Created at: April 8, 2026, 9:48 p.m.