Triple
T5689508
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Federal District (Brazil) |
E125393
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Águas Claras |
E34115
|
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: Águas Claras | Statement: [Federal District (Brazil), contains, Águas Claras]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Águas Claras Context triple: [Federal District (Brazil), contains, Águas Claras]
-
A.
Carapicuíba
Carapicuíba is a densely populated municipality in the São Paulo metropolitan area in southeastern Brazil.
-
B.
Brasília
chosen
Brasília is the modernist-planned capital city of Brazil, known for its distinctive architecture and role as a major political and administrative center in South America.
-
C.
Osasco
Osasco is a major industrial and commercial city in the metropolitan region of São Paulo, Brazil.
-
D.
Brasília Teimosa
Brasília Teimosa is a coastal neighborhood in Recife, Brazil, known for its working-class roots, history of informal settlement, and vibrant seaside community.
-
E.
Cotia
Cotia is a municipality in the metropolitan region of São Paulo, Brazil, known for its residential areas, green spaces, and proximity to the capital city.
- 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_69c0082bb19c8190823a4facd3cba79b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c023e1c6148190aeae7620bd9ee9d4 |
completed | March 22, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c05a47457c8190bc75f11a7f011a8a |
completed | March 22, 2026, 9:08 p.m. |
Created at: March 22, 2026, 3:44 p.m.