Triple
T22644298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Macaé campus |
E558914
|
entity |
| Predicate | cityServed |
P82
|
FINISHED |
| Object | Macaé |
—
|
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: Macaé | Statement: [Macaé campus, cityServed, Macaé]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Macaé Context triple: [Macaé campus, cityServed, Macaé]
-
A.
Macaé
chosen
Macaé is a coastal city in southeastern Brazil known for its offshore oil industry and role as a major hub for petroleum exploration.
-
B.
Barra Mansa
Barra Mansa is an important industrial and commercial city in the state of Rio de Janeiro, Brazil, located in the Sul Fluminense region.
-
C.
Jacareí
Jacareí is a municipality in southeastern Brazil known as part of the industrial and technological corridor within the state of São Paulo.
-
D.
Teresópolis
Teresópolis is a mountainous city in the state of Rio de Janeiro, Brazil, known for its cool climate, natural parks, and role as a popular ecotourism and weekend getaway destination.
-
E.
Maricá
Maricá is a coastal municipality in the state of Rio de Janeiro, Brazil, known for its beaches, lagoons, and growing residential communities.
- 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_69e24547f7fc819086e2c4ba3b979657 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f170366ac881909e9d1dd2e7cf7a25 |
completed | April 29, 2026, 2:43 a.m. |
Created at: April 17, 2026, 3:05 p.m.