Triple
T14609472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vila do Porto |
E342917
|
entity |
| Predicate | hasParish |
P35
|
FINISHED |
| Object |
São Pedro
São Pedro is a civil parish within the municipality of Vila do Porto in the Azores, Portugal, known for its Atlantic island setting and traditional Azorean character.
|
E1108145
|
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 Pedro | Statement: [Vila do Porto, hasParish, São Pedro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: São Pedro Context triple: [Vila do Porto, hasParish, São Pedro]
-
A.
São Pedro
São Pedro is a civil parish within the municipality of Angra do Heroísmo on Terceira Island in Portugal’s Azores archipelago.
-
B.
São Pedro
São Pedro is a settlement on the Cape Verdean island of São Vicente, known as a small coastal community near the island’s main city, Mindelo.
-
C.
São Pedro
São Pedro is a civil parish within the municipality of Ponta Delgada on São Miguel Island in Portugal’s Azores archipelago.
-
D.
São Roque
São Roque is a municipality in the state of São Paulo, Brazil, known for its wine production and scenic mountainous landscapes.
-
E.
Santo Amaro
Santo Amaro is a central neighborhood in Recife, Brazil, known for its mix of residential areas, commerce, and important urban infrastructure.
- 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 Pedro Triple: [Vila do Porto, hasParish, São Pedro]
Generated description
São Pedro is a civil parish within the municipality of Vila do Porto in the Azores, Portugal, known for its Atlantic island setting and traditional Azorean character.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: São Pedro Target entity description: São Pedro is a civil parish within the municipality of Vila do Porto in the Azores, Portugal, known for its Atlantic island setting and traditional Azorean character.
-
A.
São Pedro
São Pedro is a civil parish within the municipality of Angra do Heroísmo on Terceira Island in Portugal’s Azores archipelago.
-
B.
São Pedro
São Pedro is a civil parish within the municipality of Ponta Delgada on São Miguel Island in Portugal’s Azores archipelago.
-
C.
São Pedro
São Pedro is a settlement on the Cape Verdean island of São Vicente, known as a small coastal community near the island’s main city, Mindelo.
-
D.
São Roque
São Roque is a municipality in the state of São Paulo, Brazil, known for its wine production and scenic mountainous landscapes.
-
E.
Santo Amaro
Santo Amaro is a central neighborhood in Recife, Brazil, known for its mix of residential areas, commerce, and important urban infrastructure.
- 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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb44f0dd48190a78662b5998a6722 |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd94d22170819098df75754f5c12ab |
completed | May 8, 2026, 7:46 a.m. |
| NEDg | Description generation | batch_69fd975c51088190ac70093a591b9723 |
completed | May 8, 2026, 7:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd97f447488190958e79d776e2ed47 |
completed | May 8, 2026, 7:59 a.m. |
Created at: April 10, 2026, 1:25 a.m.