Triple
T8604373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Odemira |
E203760
|
entity |
| Predicate | hasCivilParish |
P2739
|
FINISHED |
| Object |
São Luís
São Luís is a civil parish in the municipality of Odemira, located in Portugal’s Alentejo region.
|
E749500
|
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: São Luís | Statement: [Odemira, hasCivilParish, São Luís]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: São Luís Context triple: [Odemira, hasCivilParish, São Luís]
-
A.
São Luís
São Luís is the historic capital of the Brazilian state of Maranhão, known for its well-preserved colonial architecture and rich Afro-Brazilian cultural heritage.
-
B.
Belém
Belém is a historic riverside district of Lisbon, Portugal, known for its monuments of the Age of Discoveries, including the Belém Tower and Jerónimos Monastery.
-
C.
Belém do Pará
Belém do Pará is a major port city in northern Brazil, known as the gateway to the Amazon region and an important cultural and economic center.
-
D.
Feira de Santana
Feira de Santana is a major commercial and transportation hub in northeastern Brazil and the second-largest city in the state of Bahia.
-
E.
Teresina
Teresina is the capital and largest city of the Brazilian state of Piauí, known for its hot climate and location near the confluence of the Parnaíba and Poti rivers.
- 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: São Luís Triple: [Odemira, hasCivilParish, São Luís]
Generated description
São Luís is a civil parish in the municipality of Odemira, located in Portugal’s Alentejo region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: São Luís Target entity description: São Luís is a civil parish in the municipality of Odemira, located in Portugal’s Alentejo region.
-
A.
São Luís
São Luís is the historic capital of the Brazilian state of Maranhão, known for its well-preserved colonial architecture and rich Afro-Brazilian cultural heritage.
-
B.
Belém
Belém is a historic riverside district of Lisbon, Portugal, known for its monuments of the Age of Discoveries, including the Belém Tower and Jerónimos Monastery.
-
C.
Belém do Pará
Belém do Pará is a major port city in northern Brazil, known as the gateway to the Amazon region and an important cultural and economic center.
-
D.
Feira de Santana
Feira de Santana is a major commercial and transportation hub in northeastern Brazil and the second-largest city in the state of Bahia.
-
E.
Teresina
Teresina is the capital and largest city of the Brazilian state of Piauí, known for its hot climate and location near the confluence of the Parnaíba and Poti rivers.
- 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_69ca832b56948190ba751cec255308f1 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc46dd8ff8819081ef269192047488 |
completed | March 31, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cecc839cdc819093c3cd0e44f173a2 |
completed | April 2, 2026, 8:07 p.m. |
| NEDg | Description generation | batch_69cece1681288190a6c99407bc2f0bdd |
completed | April 2, 2026, 8:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cecec118ac81909fbcafe841354c32 |
completed | April 2, 2026, 8:17 p.m. |
Created at: March 30, 2026, 6:24 p.m.