Triple
T11830775
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fort San Sebastian |
E281383
|
entity |
| Predicate | originalName |
P65
|
FINISHED |
| Object | São Sebastião |
E583617
|
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: São Sebastião | Statement: [Fort San Sebastian, originalName, São Sebastião]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: São Sebastião Context triple: [Fort San Sebastian, originalName, São Sebastião]
-
A.
São Sebastião
São Sebastião is a civil parish in the municipality of Ponta Delgada on São Miguel Island in Portugal’s Azores archipelago.
-
B.
São Sebastião
chosen
São Sebastião is a coastal municipality in the state of São Paulo, Brazil, known for its beaches, tourism, and role as a port city.
-
C.
São Sebastião do Rio de Janeiro
São Sebastião do Rio de Janeiro is the formal, historical name of the Brazilian city of Rio de Janeiro, originally dedicated to Saint Sebastian.
-
D.
Armação dos Búzios
Armação dos Búzios is a popular Brazilian coastal resort town in the state of Rio de Janeiro, renowned for its beaches, nightlife, and upscale tourism.
-
E.
Cabo Frio
Cabo Frio is a coastal city in southeastern Brazil known for its white-sand beaches, clear waters, and tourism-driven economy.
- 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_69d6ab276f8c8190b1966a0ef11349ac |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a62b75dc8190b27d24e46a262a11 |
completed | April 10, 2026, 7:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f16741d9a08190b6d6d5e59dfa41b8 |
completed | April 29, 2026, 2:04 a.m. |
Created at: April 8, 2026, 9:43 p.m.