Triple
T18363114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | José Graziano da Silva |
E439969
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Campinas |
—
|
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: Campinas | Statement: [José Graziano da Silva, workLocation, Campinas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Campinas Context triple: [José Graziano da Silva, workLocation, 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.
Barueri
Barueri is a rapidly developing municipality in the São Paulo metropolitan area of Brazil, known for its strong commercial sector and high standard of living.
-
C.
Guarulhos
Guarulhos is a major city in the São Paulo metropolitan area of Brazil, known as an important industrial and logistics hub.
-
D.
Bauru
Bauru is a city in the state of São Paulo, Brazil, known as a regional economic and educational hub that hosts a campus of the University of São Paulo.
-
E.
Ribeirão Preto
Ribeirão Preto is a major city in the state of São Paulo, Brazil, known as an important economic and cultural center with a strong agribusiness and services sector.
- 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_69d8b918221c8190a9f7b563d64ac677 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e516dd973c8190857c588ab9ad9daf |
completed | April 19, 2026, 5:54 p.m. |
Created at: April 10, 2026, 10:37 a.m.