Triple
T22990860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dragão do Mar |
E572043
|
entity |
| Predicate | locatedInCity |
P40
|
FINISHED |
| Object | Fortaleza |
—
|
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: Fortaleza | Statement: [Dragão do Mar, locatedInCity, Fortaleza]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fortaleza Context triple: [Dragão do Mar, locatedInCity, Fortaleza]
-
A.
Fortaleza
chosen
Fortaleza is a large coastal city in northeastern Brazil known for its beaches, tourism, and role as the capital of the state of Ceará.
-
B.
Saquarema
Saquarema is a coastal city in the state of Rio de Janeiro, Brazil, known for its beaches and strong surfing culture.
-
C.
São Sebastião
São Sebastião is a coastal municipality in the state of São Paulo, Brazil, known for its beaches, tourism, and role as a port city.
-
D.
São Sebastião
São Sebastião is a civil parish in the municipality of Ponta Delgada on São Miguel Island in Portugal’s Azores archipelago.
-
E.
São Sebastião
São Sebastião is a civil parish within the municipality of Rio Maior in Portugal, known for its local community and role in the region’s administrative organization.
- 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_69e245b535808190adef8a9df3c584db |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f182ee3afc819099fbc6bef0b83bd5 |
completed | April 29, 2026, 4:02 a.m. |
Created at: April 17, 2026, 3:50 p.m.