Triple
T18316885
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kasato Maru |
E438771
|
entity |
| Predicate | arrivalCountry |
P1522
|
FINISHED |
| Object | Brazil |
—
|
NE NERFINISHED |
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: Brazil | Statement: [Kasato Maru, arrivalCountry, Brazil]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brazil Context triple: [Kasato Maru, arrivalCountry, Brazil]
-
A.
Brazil
chosen
Brazil is the largest country in South America, known for its vast Amazon rainforest, diverse culture, and major cities like São Paulo and Rio de Janeiro.
-
B.
Brazil
Brazil is a 1985 dystopian science fiction film known for its darkly satirical portrayal of a bureaucratic, totalitarian society and its distinctive, surreal visual style.
-
C.
Brasyl
Brasyl is a science fiction novel by Ian McDonald that intertwines multiple timelines in Brazil to explore themes of quantum reality, culture, and globalization.
-
D.
Portela
Portela is a residential parish in the municipality of Loures, within the Lisbon metropolitan area of Portugal.
-
E.
Portela
Portela is a small settlement located within the caldera of Pico do Fogo volcano on Fogo Island in Cape Verde.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b916a2d081909e249e4902f6aad9 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5021f5f1081909fd98c8fb786c7ff |
completed | April 19, 2026, 4:26 p.m. |
Created at: April 10, 2026, 10:36 a.m.