Triple
T20724806
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nordeste Linhas Aéreas Regionais |
E509405
|
entity |
| Predicate | servedCity |
P3936
|
FINISHED |
| Object | São Luís |
—
|
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: São Luís | Statement: [Nordeste Linhas Aéreas Regionais, servedCity, São Luís]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: São Luís Context triple: [Nordeste Linhas Aéreas Regionais, servedCity, São Luís]
-
A.
São Luís
chosen
São Luís is the historic capital of the Brazilian state of Maranhão, known for its well-preserved colonial architecture and rich Afro-Brazilian cultural heritage.
-
B.
São Luís
São Luís is a civil parish in the municipality of Odemira, located in Portugal’s Alentejo region.
-
C.
Belém
Belém is a historic riverside district of Lisbon, Portugal, known for its monuments of the Age of Discoveries, including the Belém Tower and Jerónimos Monastery.
-
D.
Belém do Pará
Belém do Pará is a major port city in northern Brazil, known as the gateway to the Amazon region and an important cultural and economic center.
-
E.
Feira de Santana
Feira de Santana is a major commercial and transportation hub in northeastern Brazil and the second-largest city in the state of Bahia.
- 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_69e0b4c4cc648190b45fda6e2b20af56 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c1e662f08190917ee043612d413e |
completed | April 21, 2026, 12:16 a.m. |
Created at: April 16, 2026, 12:28 p.m.