Triple
T2068847
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southeast Region of Brazil |
E45968
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Campinas |
E61914
|
NE FINISHED |
How this triple was built (2 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: Campinas | Statement: [Southeast Region of Brazil, containsCity, Campinas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Campinas Context triple: [Southeast Region of Brazil, containsCity, Campinas]
-
A.
Campinas
chosen
Campinas is a major city in the state of São Paulo, Brazil, known as an important industrial, technological, and transportation hub in the country.
-
B.
Curitiba
Curitiba is the capital and largest city of the Brazilian state of Paraná, known for its innovative urban planning, extensive public transportation system, and high quality of life.
-
C.
Santo Amaro
Santo Amaro is a central neighborhood in Recife, Brazil, known for its mix of residential areas, commerce, and important urban infrastructure.
-
D.
Magé
Magé is a municipality in the state of Rio de Janeiro, Brazil, located in the metropolitan region of Rio de Janeiro and known for its coastal setting and historical significance.
-
E.
São Paulo
São Paulo is Brazil’s largest city and a major global financial, cultural, and industrial center in South America.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a8891b38288190abd572ccad9b6928 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb9f51a008190aead0173a9289204 |
completed | March 7, 2026, 5:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae2724f46c81909521ca75185ef5f1 |
completed | March 9, 2026, 1:49 a.m. |
Created at: March 4, 2026, 7:41 p.m.