Triple
T8655909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paraná (state) |
E205414
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Cascavel
Cascavel is a major city in western Paraná, Brazil, known as an important regional hub for agribusiness, commerce, and services.
|
E751975
|
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: Cascavel | Statement: [Paraná (state), hasCity, Cascavel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cascavel Context triple: [Paraná (state), hasCity, Cascavel]
-
A.
Lajeado
Lajeado is a city in southern Brazil known for its strong German-Brazilian cultural heritage and traditions.
-
B.
Cuiabá
Cuiabá is the capital city of Brazil’s Mato Grosso state and a primary urban hub and access point for exploring the Pantanal wetlands.
-
C.
Avaré
Avaré is a municipality in the interior of Brazil known for its agricultural activities and proximity to the Jurumirim Reservoir, located in the state of São Paulo.
-
D.
Sibaté
Sibaté is a municipality in central Colombia known for its agricultural production and proximity to Bogotá within the Cundinamarca Department.
-
E.
Botucatu
Botucatu is a municipality in southeastern Brazil known for its higher-education institutions, especially São Paulo State University (UNESP), and its surrounding sandstone cliffs and natural landscapes.
- 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: Cascavel Triple: [Paraná (state), hasCity, Cascavel]
Generated description
Cascavel is a major city in western Paraná, Brazil, known as an important regional hub for agribusiness, commerce, and services.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cascavel Target entity description: Cascavel is a major city in western Paraná, Brazil, known as an important regional hub for agribusiness, commerce, and services.
-
A.
Lajeado
Lajeado is a city in southern Brazil known for its strong German-Brazilian cultural heritage and traditions.
-
B.
Cuiabá
Cuiabá is the capital city of Brazil’s Mato Grosso state and a primary urban hub and access point for exploring the Pantanal wetlands.
-
C.
Avaré
Avaré is a municipality in the interior of Brazil known for its agricultural activities and proximity to the Jurumirim Reservoir, located in the state of São Paulo.
-
D.
Sibaté
Sibaté is a municipality in central Colombia known for its agricultural production and proximity to Bogotá within the Cundinamarca Department.
-
E.
Botucatu
Botucatu is a municipality in southeastern Brazil known for its higher-education institutions, especially São Paulo State University (UNESP), and its surrounding sandstone cliffs and natural landscapes.
- 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_69ca8350897c819086cde7596fbe5fe7 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc4844586081909b687e278496eefa |
completed | March 31, 2026, 10:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cef373a22c8190931b4107c68e7017 |
completed | April 2, 2026, 10:53 p.m. |
| NEDg | Description generation | batch_69cef52000048190bc5451cfb6446ced |
completed | April 2, 2026, 11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cef809df548190b4f9ecc709b3b065 |
completed | April 2, 2026, 11:13 p.m. |
Created at: March 30, 2026, 6:29 p.m.