Triple
T9438451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fundão Island |
E227578
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Ilha do Fundão |
E562934
|
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: Ilha do Fundão | Statement: [Fundão Island, alsoKnownAs, Ilha do Fundão]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ilha do Fundão Context triple: [Fundão Island, alsoKnownAs, Ilha do Fundão]
-
A.
Ilha do Fundão
chosen
Ilha do Fundão is an artificial island in Rio de Janeiro, Brazil, best known as the site of the main campus of the Federal University of Rio de Janeiro (UFRJ).
-
B.
Ilha do Funil
Ilha do Funil is an island located within the coastal area of Recife, a major city in northeastern Brazil.
-
C.
Ilha do Maruim
Ilha do Maruim is a small island and neighborhood area within the coastal city of Recife in northeastern Brazil.
-
D.
Ilha do Monteiro
Ilha do Monteiro is an island district within the coastal Brazilian city of Recife, known for its urban setting amid the city’s network of rivers and estuaries.
-
E.
Ilha Joana Bezerra
Ilha Joana Bezerra is an island neighborhood within the Brazilian city of Recife, known for its dense urban fabric and proximity to the city’s central areas.
- 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_69ca843884488190ad6cbe0153088234 |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7ee1c8c48190a2ae8673eee07e9a |
completed | April 1, 2026, 8:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d11053d8008190a29575149d2e027f |
completed | April 4, 2026, 1:21 p.m. |
Created at: March 30, 2026, 7:50 p.m.