Triple

T17124869
Position Surface form Disambiguated ID Type / Status
Subject Beira Interior E415565 entity
Predicate containsTown P847 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: [Beira Interior, containsTown, Fundão]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fundão
Context triple: [Beira Interior, containsTown, 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_69d886d090cc8190a39cb94992586905 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3f025fce481908e261f2e363e14f9 completed April 18, 2026, 8:57 p.m.
NED1 Entity disambiguation (via context triple) batch_6a013a12a7288190911c1be2667916c0 completed May 11, 2026, 2:08 a.m.
Created at: April 10, 2026, 5:36 a.m.