Triple
T6008455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mato Grosso do Sul |
E133771
|
entity |
| Predicate | hasBorderTown |
P847
|
FINISHED |
| Object | Ponta Porã |
E561455
|
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: Ponta Porã | Statement: [Mato Grosso do Sul, hasBorderTown, Ponta Porã]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ponta Porã Context triple: [Mato Grosso do Sul, hasBorderTown, Ponta Porã]
-
A.
Ponta Porã
chosen
Ponta Porã is a Brazilian border city in the state of Mato Grosso do Sul, known for its close integration with the Paraguayan city of Pedro Juan Caballero.
-
B.
Lajeado
Lajeado is a city in southern Brazil known for its strong German-Brazilian cultural heritage and traditions.
-
C.
Jaraguá do Sul
Jaraguá do Sul is a city in southern Brazil known for its strong German-Brazilian cultural heritage and industrial economy.
-
D.
Ijuí
Ijuí is a city in the state of Rio Grande do Sul, Brazil, known for its strong German-Brazilian cultural heritage and diverse immigrant influences.
-
E.
Nova Iguaçu
Nova Iguaçu is a large municipality in the Baixada Fluminense region of Rio de Janeiro state in southeastern Brazil, known for its role as an important industrial and residential hub in the greater Rio de Janeiro metropolitan area.
- 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_69c00872444c8190bfaf1739dcec765c |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04f154ca481909431baf4feecc16d |
completed | March 22, 2026, 8:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11367b1e88190ab8671ec48953663 |
completed | March 23, 2026, 10:18 a.m. |
Created at: March 22, 2026, 4:06 p.m.