Triple
T8655907
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paraná (state) |
E205414
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Maringá |
E651371
|
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: Maringá | Statement: [Paraná (state), hasCity, Maringá]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maringá Context triple: [Paraná (state), hasCity, Maringá]
-
A.
Maringá
chosen
Maringá is a planned, mid-20th-century city in the state of Paraná known for its green urban design, strong agricultural-based economy, and high quality of life.
-
B.
Guarapuava
Guarapuava is a city in the state of Paraná, Brazil, known for its significant population of German Brazilians and its role as an agricultural and regional economic center.
-
C.
Mourão
Mourão is a small municipality in Portugal’s Alentejo region, known for its historic castle and proximity to the Alqueva Reservoir.
-
D.
Brusque
Brusque is a city in the Brazilian state of Santa Catarina known for its strong German-Brazilian heritage and textile industry.
-
E.
Itapetininga
Itapetininga is a municipality in southeastern Brazil known for its agricultural activities and regional commercial importance within the state of São Paulo.
- 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_69ca8350897c819086cde7596fbe5fe7 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc4844586081909b687e278496eefa |
completed | March 31, 2026, 10:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cecce40c248190b4f2b21a1ecde80b |
completed | April 2, 2026, 8:09 p.m. |
Created at: March 30, 2026, 6:29 p.m.