Triple

T1788288
Position Surface form Disambiguated ID Type / Status
Subject Silves E39437 entity
Predicate hasParish P35 FINISHED
Object São Marcos da Serra
São Marcos da Serra is a rural civil parish in the municipality of Silves in Portugal’s Algarve region, known for its hilly landscape and traditional village character.
E199151 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 Marcos da Serra | Statement: [Silves, hasParish, São Marcos da Serra]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: São Marcos da Serra
Context triple: [Silves, hasParish, São Marcos da Serra]
  • A. Santo Amaro
    Santo Amaro is a central neighborhood in Recife, Brazil, known for its mix of residential areas, commerce, and important urban infrastructure.
  • B. Parnamirim
    Parnamirim is a rapidly growing city in northeastern Brazil known for its proximity to Natal and its historical role in World War II aviation.
  • C. Caucaia
    Caucaia is a coastal municipality in northeastern Brazil known for its beaches and proximity to the state capital, Fortaleza.
  • D. São José do Ribamar
    São José do Ribamar is a neighborhood of Recife, Brazil, known as part of the city’s urban coastal area in the state of Pernambuco.
  • E. Espinheiro
    Espinheiro is a central neighborhood in Recife, Brazil, known for its residential areas, commerce, and urban amenities.
  • 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 Marcos da Serra
Triple: [Silves, hasParish, São Marcos da Serra]
Generated description
São Marcos da Serra is a rural civil parish in the municipality of Silves in Portugal’s Algarve region, known for its hilly landscape and traditional village character.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: São Marcos da Serra
Target entity description: São Marcos da Serra is a rural civil parish in the municipality of Silves in Portugal’s Algarve region, known for its hilly landscape and traditional village character.
  • A. Santo Amaro
    Santo Amaro is a central neighborhood in Recife, Brazil, known for its mix of residential areas, commerce, and important urban infrastructure.
  • B. Parnamirim
    Parnamirim is a rapidly growing city in northeastern Brazil known for its proximity to Natal and its historical role in World War II aviation.
  • C. Caucaia
    Caucaia is a coastal municipality in northeastern Brazil known for its beaches and proximity to the state capital, Fortaleza.
  • D. São José do Ribamar
    São José do Ribamar is a neighborhood of Recife, Brazil, known as part of the city’s urban coastal area in the state of Pernambuco.
  • E. Espinheiro
    Espinheiro is a central neighborhood in Recife, Brazil, known for its residential areas, commerce, and urban amenities.
  • 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_69a88631854081909723959921e45c2b completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa650fd3448190a6a2c979db982cae completed March 6, 2026, 5:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada9a8a69c8190885bf06a06d3869f completed March 8, 2026, 4:54 p.m.
NEDg Description generation batch_69adaab488ec81909a340aab4916b90f completed March 8, 2026, 4:58 p.m.
NED2 Entity disambiguation (via description) batch_69adaf3cd23081909dd27c5de8e3f6d2 completed March 8, 2026, 5:17 p.m.
Created at: March 4, 2026, 7:32 p.m.