Triple

T1746777
Position Surface form Disambiguated ID Type / Status
Subject Portimão E38351 entity
Predicate near P350 FINISHED
Object Lagoa E146783 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: Lagoa | Statement: [Portimão, near, Lagoa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lagoa
Context triple: [Portimão, near, Lagoa]
  • A. Lagoa chosen
    Lagoa is a town and municipality in Portugal’s Algarve region, known for its coastal scenery, beaches, and wine production.
  • B. Argentino Lake
    Argentino Lake is a large glacial lake in Argentine Patagonia, renowned for its striking turquoise waters and proximity to famous glaciers such as Perito Moreno.
  • C. Laguna San Rafael
    Laguna San Rafael is a glacial lagoon in southern Chile famed for its dramatic icebergs and proximity to the San Rafael Glacier within Laguna San Rafael National Park.
  • D. Pampulha Lake
    Pampulha Lake is an artificial lagoon in Belo Horizonte, Brazil, renowned as the scenic centerpiece of the Pampulha Modern Ensemble, a UNESCO-listed landmark of modernist architecture and urban design.
  • E. Beira Lake
    Beira Lake is a prominent urban lake in central Colombo, Sri Lanka, known for its scenic views, religious sites, and recreational activities amid the city’s commercial district.
  • 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_69a8862b01a48190ab47209063af82d9 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa63eabdf48190878ecde3d1b1faf3 completed March 6, 2026, 5:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada0e058948190939e936af8f0e221 completed March 8, 2026, 4:16 p.m.
Created at: March 4, 2026, 7:31 p.m.