Triple
T23276108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metropolitan Region of Fortaleza |
E588722
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object | Caucaia |
—
|
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: Caucaia | Statement: [Metropolitan Region of Fortaleza, hasMunicipality, Caucaia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caucaia Context triple: [Metropolitan Region of Fortaleza, hasMunicipality, Caucaia]
-
A.
Caucaia
chosen
Caucaia is a coastal municipality in northeastern Brazil known for its beaches and proximity to the state capital, Fortaleza.
-
B.
Caieiras
Caieiras is a municipality in the metropolitan region of São Paulo, Brazil, known for its industrial activity and surrounding green areas.
-
C.
Oriximiná
Oriximiná is a large municipality in the Brazilian state of Pará, known for its Amazon rainforest areas, river systems, and significant mining and conservation sites.
-
D.
Sertãozinho
Sertãozinho is a municipality in the interior of Brazil known for its strong sugarcane-based agribusiness and ethanol production.
-
E.
Ipojuca
Ipojuca is a coastal municipality in northeastern Brazil known for its tourism-driven economy and famous beaches such as Porto de Galinhas.
- 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_69e25d16e2c08190a291de254703129e |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f19577841481909acc17bb565bae5c |
completed | April 29, 2026, 5:21 a.m. |
Created at: April 17, 2026, 4:48 p.m.