Triple
T3608781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Centro Region |
E76433
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Lourinhã
Lourinhã is a coastal municipality in western Portugal known for its rich dinosaur fossil discoveries and scenic Atlantic beaches.
|
E438500
|
NE FINISHED |
How this triple was built (4 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: Lourinhã | Statement: [Centro Region, containsCity, Lourinhã]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lourinhã Context triple: [Centro Region, containsCity, Lourinhã]
-
A.
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.
-
B.
Torres Novas
Torres Novas is a historic Portuguese city known for its medieval castle and location in the Santarém District of central Portugal.
-
C.
Caldas da Rainha
Caldas da Rainha is a historic spa and market city in western Portugal, renowned for its thermal baths, ceramics tradition, and proximity to the Atlantic coast.
-
D.
Covilhã
Covilhã is a city in central Portugal, historically known for its textile industry and as a gateway to the Serra da Estrela mountain range.
-
E.
Alcobaça
Alcobaça is a historic Portuguese city best known for its UNESCO-listed Cistercian monastery, one of the country’s most important medieval monuments.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Lourinhã Triple: [Centro Region, containsCity, Lourinhã]
Generated description
Lourinhã is a coastal municipality in western Portugal known for its rich dinosaur fossil discoveries and scenic Atlantic beaches.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lourinhã Target entity description: Lourinhã is a coastal municipality in western Portugal known for its rich dinosaur fossil discoveries and scenic Atlantic beaches.
-
A.
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.
-
B.
Torres Novas
Torres Novas is a historic Portuguese city known for its medieval castle and location in the Santarém District of central Portugal.
-
C.
Caldas da Rainha
Caldas da Rainha is a historic spa and market city in western Portugal, renowned for its thermal baths, ceramics tradition, and proximity to the Atlantic coast.
-
D.
Covilhã
Covilhã is a city in central Portugal, historically known for its textile industry and as a gateway to the Serra da Estrela mountain range.
-
E.
Alcobaça
Alcobaça is a historic Portuguese city best known for its UNESCO-listed Cistercian monastery, one of the country’s most important medieval monuments.
- F. None of above. chosen
Provenance (5 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_69ad85da0ba481908b3b48c69efe2b98 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc22a3cf081908c20b6fb55be0db2 |
completed | March 8, 2026, 6:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5f59074e881908d346937da0b056e |
completed | March 14, 2026, 11:56 p.m. |
| NEDg | Description generation | batch_69b5f65e9bec819082c33b0c066cd42b |
completed | March 14, 2026, 11:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5fa90f6a48190a27dfeb65f705225 |
completed | March 15, 2026, 12:17 a.m. |
Created at: March 8, 2026, 3:22 p.m.