Triple

T13402870
Position Surface form Disambiguated ID Type / Status
Subject Linha de Aveiro E319875 entity
Predicate startStation P389 FINISHED
Object Porto Campanhã E1019262 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: Porto Campanhã | Statement: [Linha de Aveiro, startStation, Porto Campanhã]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Porto Campanhã
Context triple: [Linha de Aveiro, startStation, Porto Campanhã]
  • A. Porto-Campanhã chosen
    Porto-Campanhã is the main railway station in Porto, Portugal, serving as a central hub for long-distance and regional train services across the country.
  • B. Porto de Mós
    Porto de Mós is a Portuguese town and municipality known for its hilltop castle and location near the limestone landscapes and caves of central Portugal.
  • C. Figueira da Foz
    Figueira da Foz is a coastal Portuguese city at the mouth of the Mondego River, known for its wide sandy beaches and seaside tourism.
  • D. Figueira da Horta
    Figueira da Horta is a small village located on the island of Maio in Cape Verde.
  • E. Portimão
    Portimão is a coastal city and popular tourist destination in southern Portugal, known for its beaches, marina, and vibrant waterfront along the Arade River.
  • 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_69d806b943cc8190b6af624d385d7e12 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dbae4982e0819087a9fcb2fa88541f completed April 12, 2026, 2:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe0ccb9c3481908820f7620102e373 completed May 8, 2026, 4:18 p.m.
Created at: April 9, 2026, 9:34 p.m.