Triple
T14293813
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | São Luís |
E354386
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Maranhão |
E469351
|
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: Maranhão | Statement: [São Luís, locatedIn, Maranhão]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maranhão Context triple: [São Luís, locatedIn, Maranhão]
-
A.
Maranhão
chosen
Maranhão is a northeastern Brazilian state known for its colonial heritage, Afro-Brazilian culture, and the Lençóis Maranhenses dune and lagoon landscapes.
-
B.
Piauí
Piauí is a state in northeastern Brazil known for its semi-arid landscapes, short Atlantic coastline, and rich archaeological sites such as those in Serra da Capivara National Park.
-
C.
Amapá
Amapá is a sparsely populated state in northern Brazil, located in the Amazon region along the Atlantic coast and bordering French Guiana.
-
D.
Sergipe
Sergipe is a small coastal state in northeastern Brazil known for its Atlantic shoreline, colonial history, and role in the broader Dutch and Portuguese colonial era.
-
E.
Alagoas
Alagoas is a small coastal state in northeastern Brazil known for its picturesque beaches, lagoons, and colonial-era history.
- 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_69d8278e17088190b328c5a9d4be74ff |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de7179368081908117a9ccfbf94fd4 |
completed | April 14, 2026, 4:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd8aa2b7908190831e6c07abcc091d |
completed | May 8, 2026, 7:02 a.m. |
Created at: April 10, 2026, 1:11 a.m.