Triple
T18070038
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ferreira do Zêzere |
E432400
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Centro |
—
|
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: Centro | Statement: [Ferreira do Zêzere, region, Centro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Centro Context triple: [Ferreira do Zêzere, region, Centro]
-
A.
Centro
chosen
Centro is a NUTS 2 statistical region in central Portugal that includes areas such as Aveiro and Coimbra.
-
B.
Centro
Centro is a municipality in the Mexican state of Tabasco whose administrative center is the city of Villahermosa.
-
C.
Centro
Centro was the former public transport authority for the West Midlands metropolitan area in England, responsible for coordinating local bus, rail, and tram services before being succeeded by Transport for West Midlands.
-
D.
Centro
Centro is the central urban district and main commercial hub of Novo Hamburgo in Rio Grande do Sul, Brazil.
-
E.
Centro
Centro is Madrid’s historic central district, known for landmarks like Puerta del Sol and Plaza Mayor and its role as the city’s main cultural and commercial hub.
- 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_69d8b9070cac81909fa9473fb1c3f1c7 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4cced29fc81908e87b4f1990fa0d8 |
completed | April 19, 2026, 12:39 p.m. |
Created at: April 10, 2026, 10:26 a.m.