Triple
T18822458
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paraná |
E460294
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Londrina |
—
|
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: Londrina | Statement: [Paraná, hasCity, Londrina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Londrina Context triple: [Paraná, hasCity, Londrina]
-
A.
Londrina
chosen
Londrina is a major city in the southern Brazilian state of Paraná known for its significant Japanese Brazilian community and strong agricultural-based economy.
-
B.
Sampa
Sampa is a celebrated Brazilian song by Caetano Veloso that poetically reflects on the city of São Paulo and its cultural atmosphere.
-
C.
Nilópolis
Nilópolis is a densely populated municipality in the state of Rio de Janeiro, Brazil, known for its urban character and strong cultural ties to the Rio de Janeiro metropolitan area.
-
D.
Uberlândia
Uberlândia is a major commercial and logistics hub in the Brazilian state of Minas Gerais, known for its agribusiness, services sector, and strategic location in the country's Southeast.
-
E.
Paraná city
Paraná city is the capital of Argentina’s Entre Ríos Province, located on the eastern bank of the Paraná River opposite Santa Fe.
- 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_69e5a6bbc7148190819252071a765975 |
completed | April 20, 2026, 4:08 a.m. |
Created at: April 10, 2026, 11:55 a.m.