Triple

T3916446
Position Surface form Disambiguated ID Type / Status
Subject El Salvador International Airport E88851 entity
Predicate locatedNear P294 FINISHED
Object San Salvador E15340 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 Salvador | Statement: [El Salvador International Airport, locatedNear, San Salvador]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Salvador
Context triple: [El Salvador International Airport, locatedNear, San Salvador]
  • A. San Salvador chosen
    San Salvador is the largest city of El Salvador and its political, cultural, and economic center.
  • B. San Pedro Sula
    San Pedro Sula is a large industrial and commercial city in northern Honduras, historically known as the country’s economic hub.
  • C. Tegucigalpa
    Tegucigalpa is the capital and largest city of Honduras, serving as its political, cultural, and economic center.
  • D. Juigalpa
    Juigalpa is a city in central Nicaragua that serves as the capital of the Chontales Department and a regional hub for agriculture and cattle ranching.
  • E. Guatemala City
    Guatemala City is the capital and largest city of Guatemala, serving as the country’s political, economic, and cultural center.
  • 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_69aed955229881909e85e73ffab1d343 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeed55fe3881909776652f0d64f66c completed March 9, 2026, 3:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69b56284b2708190858e830355f3ff24 completed March 14, 2026, 1:28 p.m.
Created at: March 9, 2026, 3:22 p.m.