Triple
T9178521
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Circo Voador |
E220261
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Rio de Janeiro |
E6266
|
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: Rio de Janeiro | Statement: [Circo Voador, city, Rio de Janeiro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rio de Janeiro Context triple: [Circo Voador, city, Rio de Janeiro]
-
A.
Rio de Janeiro
chosen
Rio de Janeiro is a major Brazilian coastal city famed for its stunning beaches, dramatic landscape, Carnival festival, and iconic Christ the Redeemer statue.
-
B.
Río de Janeiro
Río de Janeiro is a station on Buenos Aires Underground Line A in Argentina’s capital city.
-
C.
Niterói
Niterói is a coastal city in the state of Rio de Janeiro, Brazil, known for its beaches, views of Rio across the bay, and iconic modernist architecture by Oscar Niemeyer.
-
D.
Petrópolis
Petrópolis is a historic mountain city in Brazil known as the former summer residence of the Brazilian imperial family and for its well-preserved 19th-century architecture.
-
E.
São Paulo
São Paulo is Brazil’s largest city and a major global financial, cultural, and industrial center in South America.
- 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_69ca83e589948190ac9907819db11ddf |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccc24eeb988190af82dfba49aac2f8 |
completed | April 1, 2026, 6:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d19f50f1c4819099a9c511f58e9873 |
completed | April 4, 2026, 11:31 p.m. |
Created at: March 30, 2026, 7:23 p.m.