Triple
T23355617
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | West Zone of Recife |
E593038
|
entity |
| Predicate | containsNeighborhood |
P4813
|
FINISHED |
| Object | Engenho do Meio |
—
|
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: Engenho do Meio | Statement: [West Zone of Recife, containsNeighborhood, Engenho do Meio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Engenho do Meio Context triple: [West Zone of Recife, containsNeighborhood, Engenho do Meio]
-
A.
Engenho do Meio
chosen
Engenho do Meio is a neighborhood located in the city of Recife, in the state of Pernambuco, Brazil.
-
B.
Engenho de Dentro
Engenho de Dentro is a neighborhood in Rio de Janeiro, Brazil, known for hosting the Estádio Nilton Santos football stadium.
-
C.
Poço das Antas
Poço das Antas is a small municipality in the Vale do Taquari region of Rio Grande do Sul, Brazil, known for its rural landscape and agricultural activities.
-
D.
Vila-seca
Vila-seca is a coastal municipality in Catalonia, Spain, known for its tourism, proximity to Tarragona, and educational facilities including a campus of Rovira i Virgili University.
-
E.
Afogados
Afogados is a populous neighborhood in the Brazilian city of Recife, known for its busy commercial areas and dense urban character.
- 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_69e25d24d2a4819092e6ede74c2a918d |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f19a176b548190bf5a08bb2585344d |
completed | April 29, 2026, 5:41 a.m. |
Created at: April 17, 2026, 5:26 p.m.