Triple
T4751482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alcântara |
E105485
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | São Luís |
E354386
|
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 Luís | Statement: [Alcântara, locatedNear, São Luís]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: São Luís Context triple: [Alcântara, locatedNear, 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.
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.
-
C.
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.
-
D.
Aracaju
Aracaju is a coastal city in northeastern Brazil known for its planned urban layout, beaches, and role as an administrative and economic center.
-
E.
Maceió
Maceió is a coastal city in northeastern Brazil known for its white-sand beaches, turquoise waters, and vibrant tourism industry.
- 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_69bd43f07fa48190954317d01600994a |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd64c97b548190815083f2f8df907c |
completed | March 20, 2026, 3:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be922efb7c8190a7ea9a7c9aa5503d |
completed | March 21, 2026, 12:42 p.m. |
Created at: March 20, 2026, 1:20 p.m.