Triple

T5718792
Position Surface form Disambiguated ID Type / Status
Subject Lago Xolotlán E126088 entity
Predicate borders P224 FINISHED
Object Managua Department E97833 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: Managua Department | Statement: [Lago Xolotlán, borders, Managua Department]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Managua Department
Context triple: [Lago Xolotlán, borders, Managua Department]
  • A. Managua Department chosen
    Managua Department is an administrative region of Nicaragua that includes the nation’s capital city, Managua, and serves as its political and economic center.
  • B. Masaya Department
    Masaya Department is an administrative region in western Nicaragua known for its active Masaya Volcano, traditional handicrafts, and cultural festivals.
  • C. Boaco Department
    Boaco Department is an inland administrative region of central Nicaragua known for its hilly terrain, cattle ranching, and agricultural economy.
  • D. Cesar Department
    Cesar Department is an administrative region in northern Colombia known for its capital Valledupar and its strong cultural association with vallenato music.
  • E. Chinandega Department
    Chinandega Department is a northwestern region of Nicaragua known for its agricultural production, Pacific coastline, and proximity to major lakes and volcanoes.
  • 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_69c0082e3d548190950169847b43043b completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c024e1ec7c8190a08e1b7954db2a9d completed March 22, 2026, 5:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69c05a7db0788190b4a5e7b5d9c94588 completed March 22, 2026, 9:09 p.m.
Created at: March 22, 2026, 3:46 p.m.