Triple
T12007323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sado River area |
E285814
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Alentejo Litoral |
E219925
|
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: Alentejo Litoral | Statement: [Sado River area, partOf, Alentejo Litoral]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alentejo Litoral Context triple: [Sado River area, partOf, Alentejo Litoral]
-
A.
Alentejo Litoral
chosen
Alentejo Litoral is a coastal subregion of Portugal’s Alentejo known for its Atlantic beaches, rural landscapes, and traditional agriculture.
-
B.
Baixo Alentejo
Baixo Alentejo is a sparsely populated, predominantly rural subregion in southern Portugal known for its rolling plains, cork oak forests, and traditional agriculture.
-
C.
Alto Alentejo
Alto Alentejo is a subregion in northern Alentejo, Portugal, known for its historic towns, rural landscapes, and traditional agriculture.
-
D.
Alentejo Central
Alentejo Central is a subregion in southern Portugal known for its historic towns, rolling plains, and wine production within the broader Alentejo region.
-
E.
Alentejo
Alentejo is a large, sparsely populated region in southern Portugal known for its rolling plains, cork oak forests, vineyards, and historic whitewashed towns.
- 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_69d6ab45a368819084fce08bf0dc3705 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903c5cfc08190821e4b2940c51416 |
completed | April 10, 2026, 2:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60a5ccac481908c61fc1109cc6ef6 |
completed | May 2, 2026, 2:29 p.m. |
Created at: April 8, 2026, 9:46 p.m.