Triple

T14011736
Position Surface form Disambiguated ID Type / Status
Subject Covilhã E337094 entity
Predicate connectedByRailTo P848 FINISHED
Object Fundão E374149 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: Fundão | Statement: [Covilhã, connectedByRailTo, Fundão]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fundão
Context triple: [Covilhã, connectedByRailTo, Fundão]
  • A. Fundão chosen
    Fundão is a municipality in central Portugal known for its agricultural production, particularly cherries, and its growing role as a regional technology and innovation hub.
  • B. Fundão
    Fundão is a small coastal municipality in southeastern Brazil known for its beaches and proximity to the state capital, Vitória, in Espírito Santo.
  • C. Vargem Grande
    Vargem Grande is a largely residential and semi-rural neighborhood located in the western part of Rio de Janeiro, Brazil, known for its green areas and proximity to natural reserves.
  • D. Canindé
    Canindé is a municipality in the Brazilian state of Ceará known for its major religious pilgrimages honoring Saint Francis of Assisi.
  • E. Guararema
    Guararema is a Brazilian municipality in the state of São Paulo, known for its preserved historic center, riverside landscapes, and eco-tourism attractions.
  • 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_69d81c645c5c8190b1fd16a285a1b78a completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2ed5cfd0819085b9c860b119a9de completed April 14, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69fbacaa16e88190995fd86951fb54e6 completed May 6, 2026, 9:03 p.m.
Created at: April 9, 2026, 10:19 p.m.