Triple

T3313462
Position Surface form Disambiguated ID Type / Status
Subject Alcoutim E69625 entity
Predicate hasSettlement P1068 FINISHED
Object Pereiro
Pereiro is a small settlement within the municipality of Alcoutim in southeastern Portugal, near the Spanish border.
 E347329 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: Pereiro | Statement: [Alcoutim, hasSettlement, Pereiro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Pereiro
Context triple: [Alcoutim, hasSettlement, Pereiro]
  • A. Caleruega
    Caleruega is a small town in the province of Burgos, Spain, best known as the birthplace of Saint Dominic, founder of the Dominican Order.
  • B. Cosío
    Cosío is a small municipality and town located in the northern part of the Mexican state of Aguascalientes.
  • C. Nalón
    The Nalón is a major river in Asturias, northern Spain, known for flowing through mountainous landscapes and historically supporting regional industry and mining.
  • D. Vegueta
    Vegueta is the historic old quarter of Las Palmas de Gran Canaria, known for its colonial architecture, cobbled streets, and cultural landmarks.
  • E. Lerma
    Lerma is a municipality in the State of Mexico, west of Mexico City, known for its industrial zones, residential developments, and proximity to major transportation routes.
  • 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: Pereiro
Triple: [Alcoutim, hasSettlement, Pereiro]
Generated description
Pereiro is a small settlement within the municipality of Alcoutim in southeastern Portugal, near the Spanish border.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Pereiro
Target entity description: Pereiro is a small settlement within the municipality of Alcoutim in southeastern Portugal, near the Spanish border.
  • A. Caleruega
    Caleruega is a small town in the province of Burgos, Spain, best known as the birthplace of Saint Dominic, founder of the Dominican Order.
  • B. Cosío
    Cosío is a small municipality and town located in the northern part of the Mexican state of Aguascalientes.
  • C. Nalón
    The Nalón is a major river in Asturias, northern Spain, known for flowing through mountainous landscapes and historically supporting regional industry and mining.
  • D. Vegueta
    Vegueta is the historic old quarter of Las Palmas de Gran Canaria, known for its colonial architecture, cobbled streets, and cultural landmarks.
  • E. Lerma
    Lerma is a municipality in the State of Mexico, west of Mexico City, known for its industrial zones, residential developments, and proximity to major transportation routes.
  • 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_69ad85a0bb048190a5458d2738012d61 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb0ef548481908b3aabc7052c70d8 completed March 8, 2026, 5:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2f3f760348190abd8854c369cb41b completed March 12, 2026, 5:12 p.m.
NEDg Description generation batch_69b2fa1dafe8819094905ac930fd1761 completed March 12, 2026, 5:38 p.m.
NED2 Entity disambiguation (via description) batch_69b312b6e224819080957998acbed524 completed March 12, 2026, 7:23 p.m.
Created at: March 8, 2026, 3:11 p.m.