Triple

T4250183
Position Surface form Disambiguated ID Type / Status
Subject Gulf of Fonseca E95828 entity
Predicate hasPort P35 FINISHED
Object San Lorenzo E307237 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: San Lorenzo | Statement: [Gulf of Fonseca, hasPort, San Lorenzo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Lorenzo
Context triple: [Gulf of Fonseca, hasPort, San Lorenzo]
  • A. San Lorenzo chosen
    San Lorenzo is a coastal municipality on the island province of Guimaras in the Philippines, known for its rural communities and agricultural landscape.
  • B. San Lorenzo
    San Lorenzo is a municipality in the central-eastern region of Puerto Rico known for its rural landscapes and small-town character.
  • C. Barracas
    Barracas is a traditional working-class neighborhood in Buenos Aires, Argentina, known for its historic architecture, industrial past, and strong local identity.
  • D. Boca Juniors
    Boca Juniors is one of Argentina’s most famous and successful football clubs, renowned for its passionate fan base, numerous domestic and international titles, and iconic La Bombonera stadium.
  • E. Horizontina
    Horizontina is a municipality in the state of Rio Grande do Sul in southern Brazil, known as the birthplace of supermodel Gisele Bündchen.
  • 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_69b3453f759881909b91f01a1e82c036 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b34e9f11008190a0021e0ad730a79d completed March 12, 2026, 11:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5a87fdca481908bd2c80b10d0dd3d completed March 14, 2026, 6:27 p.m.
Created at: March 12, 2026, 11:06 p.m.