Triple

T6217581
Position Surface form Disambiguated ID Type / Status
Subject Bertioga E139027 entity
Predicate borderedBy P224 FINISHED
Object São Sebastião
São Sebastião is a coastal municipality in the state of São Paulo, Brazil, known for its beaches, tourism, and role as a port city.
 E583617 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 Sebastião | Statement: [Bertioga, borderedBy, São Sebastião]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: São Sebastião
Context triple: [Bertioga, borderedBy, São Sebastião]
  • A. São Sebastião
    São Sebastião is a civil parish in the municipality of Ponta Delgada on São Miguel Island in Portugal’s Azores archipelago.
  • B. Cabo Frio
    Cabo Frio is a coastal city in southeastern Brazil known for its white-sand beaches, clear waters, and tourism-driven economy.
  • C. Porto Judeu
    Porto Judeu is a civil parish on Terceira Island in the Azores, Portugal, known for its coastal setting and traditional Azorean character.
  • D. Ponta do Sol
    Ponta do Sol is a coastal town on the island of Santo Antão in Cape Verde, known for its dramatic oceanfront setting and role as a local administrative and fishing center.
  • E. Santa Cruz das Flores
    Santa Cruz das Flores is the main town and administrative center of Flores Island in Portugal’s Azores archipelago.
  • 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 Sebastião
Triple: [Bertioga, borderedBy, São Sebastião]
Generated description
São Sebastião is a coastal municipality in the state of São Paulo, Brazil, known for its beaches, tourism, and role as a port city.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: São Sebastião
Target entity description: São Sebastião is a coastal municipality in the state of São Paulo, Brazil, known for its beaches, tourism, and role as a port city.
  • A. São Sebastião
    São Sebastião is a civil parish in the municipality of Ponta Delgada on São Miguel Island in Portugal’s Azores archipelago.
  • B. Cabo Frio
    Cabo Frio is a coastal city in southeastern Brazil known for its white-sand beaches, clear waters, and tourism-driven economy.
  • C. Porto Judeu
    Porto Judeu is a civil parish on Terceira Island in the Azores, Portugal, known for its coastal setting and traditional Azorean character.
  • D. Ponta do Sol
    Ponta do Sol is a coastal town on the island of Santo Antão in Cape Verde, known for its dramatic oceanfront setting and role as a local administrative and fishing center.
  • E. Santa Cruz das Flores
    Santa Cruz das Flores is the main town and administrative center of Flores Island in Portugal’s Azores archipelago.
  • 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_69c008aecb0c81909984b48f733ce8ae completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c062a35e308190be25c41b02704411 completed March 22, 2026, 9:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69c5e3dd76508190aba82a4a74c74bea completed March 27, 2026, 1:56 a.m.
NEDg Description generation batch_69c5eb3db09c81909fd0e11e18d47394 completed March 27, 2026, 2:28 a.m.
NED2 Entity disambiguation (via description) batch_69c5ebb252c88190bfb697e5804767fa completed March 27, 2026, 2:30 a.m.
Created at: March 22, 2026, 4:21 p.m.