Triple

T20070896
Position Surface form Disambiguated ID Type / Status
Subject Guatemalan Armed Forces E499733 entity
Predicate garrison P75 FINISHED
Object Guatemala City NE NERFINISHED

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: Guatemala City | Statement: [Guatemalan Armed Forces, garrison, Guatemala City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Guatemala City
Context triple: [Guatemalan Armed Forces, garrison, Guatemala City]
  • A. Guatemala City chosen
    Guatemala City is the capital and largest city of Guatemala, serving as the country’s political, economic, and cultural center.
  • B. Siguatepeque
    Siguatepeque is a Honduran city known as an important commercial and agricultural center in the country’s central highlands.
  • C. Santiago Atitlán
    Santiago Atitlán is a traditional Tz'utujil Maya town in Guatemala known for its vibrant indigenous culture, crafts, and scenic location on the shores of Lake Atitlán.
  • D. San Pedro Sula
    San Pedro Sula is a large industrial and commercial city in northern Honduras, historically known as the country’s economic hub.
  • E. San Salvador
    San Salvador is the largest city of El Salvador and its political, cultural, and economic center.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69da627770948190997f486f9a2e370f completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e6643798a4819081fa4e71c74b47bc completed April 20, 2026, 5:36 p.m.
Created at: April 11, 2026, 3:40 p.m.