Triple
T9964374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alto Alentejo |
E195643
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Portalegre |
E211422
|
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: Portalegre | Statement: [Alto Alentejo, hasCity, Portalegre]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Portalegre Context triple: [Alto Alentejo, hasCity, Portalegre]
-
A.
Portalegre
chosen
Portalegre is a historic city in eastern Portugal known for its medieval architecture, hilltop setting near the São Mamede mountains, and traditional Alentejo culture.
-
B.
Sernancelhe
Sernancelhe is a municipality in northern Portugal known for its historic granite architecture, religious heritage, and scenic rural landscapes.
-
C.
Lamego
Lamego is a historic city in northern Portugal known for its baroque Sanctuary of Our Lady of Remedies and its location in the Douro wine region.
-
D.
Lourinhã
Lourinhã is a coastal municipality in western Portugal known for its rich dinosaur fossil discoveries and scenic Atlantic beaches.
-
E.
Pero da Covilhã
Pero da Covilhã was a 15th-century Portuguese explorer and diplomat known for his overland journeys to India and Ethiopia that helped pave the way for the sea route to India.
- 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_69ca82ebd1288190912f9e4482d1fa35 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb71a33b48190a18c1a9023f249d2 |
completed | April 2, 2026, 12:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2b5d295908190a064fb72d65b6e24 |
completed | April 5, 2026, 7:19 p.m. |
Created at: March 30, 2026, 8:47 p.m.