Triple
T18816845
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maranello |
E460157
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Mogi das Cruzes |
—
|
NE NERFINISHED |
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: Mogi das Cruzes | Statement: [Maranello, twinTown, Mogi das Cruzes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mogi das Cruzes Context triple: [Maranello, twinTown, Mogi das Cruzes]
-
A.
Mogi das Cruzes
chosen
Mogi das Cruzes is a municipality in southeastern Brazil known as part of the Greater São Paulo metropolitan area and recognized for its industrial activity and agricultural production.
-
B.
São Caetano do Sul
São Caetano do Sul is a highly urbanized and affluent city in the São Paulo metropolitan region of Brazil, known for its high quality of life and strong industrial and service sectors.
-
C.
São Bernardo do Campo
São Bernardo do Campo is a major industrial city in Brazil known as a key center of the automotive industry within the São Paulo metropolitan area.
-
D.
Guarulhos
Guarulhos is a major city in the São Paulo metropolitan area of Brazil, known as an important industrial and logistics hub.
-
E.
Osasco
Osasco is a major industrial and commercial city in the metropolitan region of São Paulo, Brazil.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dcf94c288190a06dea029ae4b223 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5a6b620248190afa21a6ce61e2cff |
completed | April 20, 2026, 4:08 a.m. |
Created at: April 10, 2026, 11:55 a.m.