Triple
T22431382
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Potiguar |
E554506
|
entity |
| Predicate | containsMunicipality |
P852
|
FINISHED |
| Object | Lucrécia |
—
|
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: Lucrécia | Statement: [Central Potiguar, containsMunicipality, Lucrécia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lucrécia Context triple: [Central Potiguar, containsMunicipality, Lucrécia]
-
A.
Lucrécia
chosen
Lucrécia is a small municipality located in the Central Potiguar region of the state of Rio Grande do Norte in northeastern Brazil.
-
B.
Eugênia
Eugênia is a Portuguese given name, equivalent to Eugenia, commonly used in Brazil and other Lusophone countries.
-
C.
Catarina
Catarina is a small Nicaraguan town and municipality known for its scenic views over Laguna de Apoyo and its traditional plant and craft markets.
-
D.
Rosana
Rosana is a municipality in the state of São Paulo, Brazil, known for hosting a campus of São Paulo State University (UNESP).
-
E.
Rosana
Rosana is a Brazilian professional footballer known for her successful international career and contributions to top women’s clubs, including Avaldsnes IL.
- 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_69e11e4f2d0c819091aa3558ea2ee630 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15a32139481909baaf9275f5e0257 |
completed | April 29, 2026, 1:09 a.m. |
Created at: April 16, 2026, 8:47 p.m.