Triple

T17045093
Position Surface form Disambiguated ID Type / Status
Subject Santarém District E413546 entity
Predicate hasMunicipality P847 FINISHED
Object Entroncamento E374173 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: Entroncamento | Statement: [Santarém District, hasMunicipality, Entroncamento]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Entroncamento
Context triple: [Santarém District, hasMunicipality, Entroncamento]
  • A. Entroncamento chosen
    Entroncamento is a Portuguese railway junction city in the Centro Region, known for its strategic location on the country’s main rail lines.
  • B. Encruzilhada
    Encruzilhada is a neighborhood in the city of Recife, Brazil, known for its busy commercial areas and urban residential character.
  • C. Felling
    Felling is a town in Tyne and Wear, England, situated on the south bank of the River Tyne near Gateshead and Newcastle upon Tyne.
  • D. Divisadero
    Divisadero is a nonlinear, character-driven novel by Michael Ondaatje that interweaves past and present across California and France to explore memory, identity, and the fractures within a makeshift family.
  • E. Divisadero
    Divisadero is a popular lookout and tourist stop in Mexico’s Copper Canyon region, known for its dramatic canyon views and access to scenic train routes.
  • 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_69d886cd18288190b006abab23f811b7 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3da9d7e988190a5e3991c7123f9b0 completed April 18, 2026, 7:25 p.m.
NED1 Entity disambiguation (via context triple) batch_6a01233cd3d48190b002951881ef670b completed May 11, 2026, 12:30 a.m.
Created at: April 10, 2026, 5:33 a.m.