Triple
T5517840
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tietê River |
E144727
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object | Santana de Parnaíba |
E297770
|
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: Santana de Parnaíba | Statement: [Tietê River, passesNear, Santana de Parnaíba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Santana de Parnaíba Context triple: [Tietê River, passesNear, Santana de Parnaíba]
-
A.
Santana de Parnaíba
chosen
Santana de Parnaíba is a historic municipality in the São Paulo metropolitan region of Brazil, known for its well-preserved colonial architecture and cultural heritage.
-
B.
Arapiraca
Arapiraca is a major city in the Brazilian state of Alagoas, known as an important regional commercial and agricultural center.
-
C.
Caieiras
Caieiras is a municipality in the metropolitan region of São Paulo, Brazil, known for its industrial activity and surrounding green areas.
-
D.
Caucaia
Caucaia is a coastal municipality in northeastern Brazil known for its beaches and proximity to the state capital, Fortaleza.
-
E.
Parnamirim
Parnamirim is a rapidly growing city in northeastern Brazil known for its proximity to Natal and its historical role in World War II aviation.
- 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_69c008f77ff88190b0cd50ca207295d1 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f6cefe48190bfda90d6afab8468 |
completed | March 22, 2026, 4:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c027e1ed4c81908f670286556ced81 |
completed | March 22, 2026, 5:33 p.m. |
Created at: March 22, 2026, 3:33 p.m.