Triple

T3984558
Position Surface form Disambiguated ID Type / Status
Subject Pico Ruivo E86839 entity
Predicate hasTrailhead P3625 FINISHED
Object Achada do Teixeira
Achada do Teixeira is a popular mountain plateau and viewpoint in Madeira, Portugal, serving as a main access point for hikes into the island’s central highlands.
 E404858 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: Achada do Teixeira | Statement: [Pico Ruivo, hasTrailhead, Achada do Teixeira]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Achada do Teixeira
Context triple: [Pico Ruivo, hasTrailhead, Achada do Teixeira]
  • A. Cabaceiras
    Cabaceiras is a historic town in the Brazilian state of Paraíba, known for its well-preserved colonial architecture and frequent use as a filming location for movies and television.
  • B. Pedreira
    Pedreira is a municipality in the state of São Paulo, Brazil, known for its ceramics industry and decorative household goods.
  • C. Pilão Cão
    Pilão Cão is a small rural village located on the island of Maio in Cape Verde.
  • D. Poço da Panela
    Poço da Panela is a historic and traditionally residential neighborhood located along the Capibaribe River in the city of Recife, Brazil.
  • E. Taboão da Serra
    Taboão da Serra is a densely populated municipality in the São Paulo metropolitan area in southeastern Brazil.
  • 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: Achada do Teixeira
Triple: [Pico Ruivo, hasTrailhead, Achada do Teixeira]
Generated description
Achada do Teixeira is a popular mountain plateau and viewpoint in Madeira, Portugal, serving as a main access point for hikes into the island’s central highlands.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Achada do Teixeira
Target entity description: Achada do Teixeira is a popular mountain plateau and viewpoint in Madeira, Portugal, serving as a main access point for hikes into the island’s central highlands.
  • A. Cabaceiras
    Cabaceiras is a historic town in the Brazilian state of Paraíba, known for its well-preserved colonial architecture and frequent use as a filming location for movies and television.
  • B. Pedreira
    Pedreira is a municipality in the state of São Paulo, Brazil, known for its ceramics industry and decorative household goods.
  • C. Pilão Cão
    Pilão Cão is a small rural village located on the island of Maio in Cape Verde.
  • D. Poço da Panela
    Poço da Panela is a historic and traditionally residential neighborhood located along the Capibaribe River in the city of Recife, Brazil.
  • E. Taboão da Serra
    Taboão da Serra is a densely populated municipality in the São Paulo metropolitan area in southeastern Brazil.
  • 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_69aed93fd9d4819085d3b2137d2346cb completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aef9de58d48190969f354a1bf0df94 completed March 9, 2026, 4:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69b540284d548190821d37b68974a2d2 completed March 14, 2026, 11:02 a.m.
NEDg Description generation batch_69b54126234c8190990cf6df84a83e92 completed March 14, 2026, 11:06 a.m.
NED2 Entity disambiguation (via description) batch_69b5458d736481908ca826fa4fd01b8c completed March 14, 2026, 11:25 a.m.
Created at: March 9, 2026, 3:33 p.m.