Triple
T15029144
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sever do Vouga |
E378295
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Centro |
E816316
|
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: Centro | Statement: [Sever do Vouga, region, Centro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Centro Context triple: [Sever do Vouga, region, Centro]
-
A.
Centro
Centro is a municipality in the Mexican state of Tabasco whose administrative center is the city of Villahermosa.
-
B.
Centro
chosen
Centro is a NUTS 2 statistical region in central Portugal that includes areas such as Aveiro and Coimbra.
-
C.
Centro
Centro is the historic downtown district of São Paulo, Brazil, known as the city’s main commercial, financial, and cultural hub.
-
D.
Centro
Centro is the primary public bus service brand operating in the Central New York region, providing local and regional transit across cities such as Syracuse and its surrounding communities.
-
E.
Centro
Centro is the central urban district and main commercial hub of Novo Hamburgo in Rio Grande do Sul, Brazil.
- 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_69d85cd46b2c819090d054c27787f677 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7e0e8c88190ac6f5786b4d4040f |
completed | April 15, 2026, 12:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9dd967588190821cf47e9734db21 |
completed | May 9, 2026, 2:37 a.m. |
Created at: April 10, 2026, 2:59 a.m.