Triple
T6348187
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mundaú Lagoon |
E142798
|
entity |
| Predicate | hasShore |
P969
|
FINISHED |
| Object | city of Maceió |
E171696
|
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: city of Maceió | Statement: [Mundaú Lagoon, hasShore, city of Maceió]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: city of Maceió Context triple: [Mundaú Lagoon, hasShore, city of Maceió]
-
A.
Maceió
chosen
Maceió is a coastal city in northeastern Brazil known for its white-sand beaches, turquoise waters, and vibrant tourism industry.
-
B.
Aracaju
Aracaju is a coastal city in northeastern Brazil known for its planned urban layout, beaches, and role as an administrative and economic center.
-
C.
Recife
Recife is a major coastal city in northeastern Brazil known for its historic colonial architecture, extensive waterways, and role as an important cultural and economic center.
-
D.
Teresina
Teresina is the capital and largest city of the Brazilian state of Piauí, known for its hot climate and location near the confluence of the Parnaíba and Poti rivers.
-
E.
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.
- 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_69c008d6dcbc8190aa1c2f1fd8916b42 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067bba1988190b51f0a22e4279e1b |
completed | March 22, 2026, 10:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6044b33fc8190a214c6615d072715 |
completed | March 27, 2026, 4:15 a.m. |
Created at: March 22, 2026, 4:31 p.m.