Triple

T15568838
Position Surface form Disambiguated ID Type / Status
Subject Aveiro District E374185 entity
Predicate containsCity P294 FINISHED
Object Espinho E699875 NE FINISHED

How this triple was built (2 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: Espinho | Statement: [Aveiro District, containsCity, Espinho]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Espinho
Context triple: [Aveiro District, containsCity, Espinho]
  • A. Espinho chosen
    Espinho is a coastal city and municipality in northern Portugal, known for its beaches, casino, and traditional fishing heritage.
  • B. Carvoeiro
    Carvoeiro is a picturesque coastal village in southern Portugal known for its dramatic cliffs, sandy beaches, and role as a popular holiday destination.
  • C. Sernancelhe
    Sernancelhe is a municipality in northern Portugal known for its historic granite architecture, religious heritage, and scenic rural landscapes.
  • D. Porto Covo
    Porto Covo is a small coastal village in Portugal’s Alentejo region, known for its picturesque beaches, rugged cliffs, and traditional whitewashed houses.
  • E. Sertã
    Sertã is a municipality and town in central Portugal known for its forested landscapes, river beaches, and traditional cuisine.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d85ccd575081908909b71a3f3e3a61 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04dde90b081908284d9258d4462e3 completed April 16, 2026, 2:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff56c231e0819083d6032eb21114b2 completed May 9, 2026, 3:46 p.m.
Created at: April 10, 2026, 4:10 a.m.