Triple
T15783861
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mineirão |
E382685
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Belo Horizonte |
E112920
|
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: Belo Horizonte | Statement: [Mineirão, locatedIn, Belo Horizonte]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belo Horizonte Context triple: [Mineirão, locatedIn, Belo Horizonte]
-
A.
Belo Horizonte
chosen
Belo Horizonte is the capital and largest city of the Brazilian state of Minas Gerais, known for its modernist architecture, surrounding mountains, and vibrant cultural and economic life.
-
B.
São Paulo
São Paulo is Brazil’s largest city and a major global financial, cultural, and industrial center in South America.
-
C.
Goiânia
Goiânia is the capital and largest city of the Brazilian state of Goiás, known as a major regional center for agriculture, industry, and services in central Brazil.
-
D.
Guarulhos
Guarulhos is a major city in the São Paulo metropolitan area of Brazil, known as an important industrial and logistics hub.
-
E.
Juiz de Fora
Juiz de Fora is a major industrial and university city in the state of Minas Gerais, known for its strategic location between Rio de Janeiro, São Paulo, and Belo Horizonte.
- 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_69d86da16e188190b89af699f1ed0bfe |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e05401c4788190a31c180953433db9 |
completed | April 16, 2026, 3:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff90a4661481909d04bcb9f5043a6b |
completed | May 9, 2026, 7:53 p.m. |
Created at: April 10, 2026, 4:48 a.m.