Triple

T1876639
Position Surface form Disambiguated ID Type / Status
Subject Sugarloaf Mountain E39158 entity
Predicate near P350 FINISHED
Object Morro da Urca E209268 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: Morro da Urca | Statement: [Sugarloaf Mountain, near, Morro da Urca]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Morro da Urca
Context triple: [Sugarloaf Mountain, near, Morro da Urca]
  • A. Morro de Môco
    Morro de Môco is the tallest mountain in Angola, a prominent peak in the country's central highlands.
  • B. Urca chosen
    Urca is a picturesque, upscale seaside neighborhood in Rio de Janeiro, Brazil, known for its tranquil streets, historic architecture, and scenic views of Guanabara Bay.
  • C. São Jorge hill
    São Jorge hill is the prominent elevation in Lisbon’s historic center that hosts the iconic São Jorge Castle and overlooks the city and the Tagus River.
  • D. Torre do Pinhão
    Torre do Pinhão is a civil parish in the municipality of Sabrosa, located in Portugal’s Douro wine region.
  • E. Morro Branco
    Morro Branco is a famous beach in the Brazilian state of Ceará, known for its colorful sand cliffs, labyrinthine sand formations, and scenic coastal landscapes.
  • 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_69a8862f7074819096afe7fe65e179e9 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb0da543481908ab25806e6b80375 completed March 7, 2026, 5 a.m.
NED1 Entity disambiguation (via context triple) batch_69adeae228008190a0d427c74fd37511 completed March 8, 2026, 9:32 p.m.
Created at: March 4, 2026, 7:34 p.m.