Triple

T9249380
Position Surface form Disambiguated ID Type / Status
Subject Port of Topolobampo E222282 entity
Predicate locatedIn P40 FINISHED
Object Topolobampo E154229 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: Topolobampo | Statement: [Port of Topolobampo, locatedIn, Topolobampo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Topolobampo
Context triple: [Port of Topolobampo, locatedIn, Topolobampo]
  • A. Topolobampo chosen
    Topolobampo is a major Pacific coast port city in northwestern Mexico, serving as an important hub for maritime trade and ferry connections in the state of Sinaloa.
  • B. Yucpa
    Yucpa is an indigenous language spoken by the Yukpa people of the Sierra de Perijá region along the Colombia–Venezuela border.
  • C. Zapote
    Zapote is a district of San José, Costa Rica, known for housing important government buildings and urban residential areas.
  • D. Cholula
    Cholula is a historic Mexican city famed for its Great Pyramid and rich pre-Hispanic and colonial heritage.
  • E. Tejipió
    Tejipió is a neighborhood in the city of Recife, Brazil, known as part of the urban fabric of the state capital of Pernambuco.
  • 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_69ca841d2b18819089f9faf5b2c2aec0 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd05f7e9848190939f9199d0c1a572 completed April 1, 2026, 11:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69d077fed7888190a5d36bc2ee4c2bd2 completed April 4, 2026, 2:31 a.m.
Created at: March 30, 2026, 7:31 p.m.