Triple
T12314000
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line 7-Rubi |
E293551
|
entity |
| Predicate | servesMunicipality |
P3936
|
FINISHED |
| Object | Caieiras |
E299855
|
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: Caieiras | Statement: [Line 7-Rubi, servesMunicipality, Caieiras]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caieiras Context triple: [Line 7-Rubi, servesMunicipality, Caieiras]
-
A.
Caieiras
chosen
Caieiras is a municipality in the metropolitan region of São Paulo, Brazil, known for its industrial activity and surrounding green areas.
-
B.
Caucaia
Caucaia is a coastal municipality in northeastern Brazil known for its beaches and proximity to the state capital, Fortaleza.
-
C.
Igarassu
Igarassu is one of Brazil’s oldest colonial towns, known for its historic churches and coastal location in the northeastern state of Pernambuco.
-
D.
Arujá
Arujá is a municipality in the state of São Paulo, Brazil, known for its green areas and residential character within the Greater São Paulo region.
-
E.
Cabaceiras
Cabaceiras is a historic town in the Brazilian state of Paraíba, known for its well-preserved colonial architecture and frequent use as a filming location for movies and television.
- 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_69d6ab6a2b50819082f6aedd32ed608a |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f03d3c88190baedffb83465bff8 |
completed | April 10, 2026, 6:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6685205608190b504ab6e7c73ee51 |
completed | May 2, 2026, 9:10 p.m. |
Created at: April 8, 2026, 9:53 p.m.