Triple

T22672251
Position Surface form Disambiguated ID Type / Status
Subject Sipakapense language E560247 entity
Predicate hasAlternativeName P39 FINISHED
Object Sipacapa 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: Sipacapa | Statement: [Sipakapense language, hasAlternativeName, Sipacapa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sipacapa
Context triple: [Sipakapense language, hasAlternativeName, Sipacapa]
  • A. Sipacapa chosen
    Sipacapa is a highland municipality in the San Marcos department of western Guatemala, known for its predominantly Sipakapense Maya population and traditional indigenous culture.
  • B. Pacasmayo
    Pacasmayo is a coastal city in northern Peru known for its long pier, surfing beaches, and colonial-era architecture.
  • C. Jicalapa
    Jicalapa is a small municipality in western El Salvador known for its rural character and location within the coastal La Libertad region.
  • D. Amapala
    Amapala is a coastal town and former major Pacific port of Honduras located on El Tigre Island in the Gulf of Fonseca.
  • E. Ogáxpa
    Ogáxpa is the traditional name used by the Quapaw people to refer to themselves or their community in their own language.
  • 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_69e2454bfd00819099115715a22cb057 completed April 17, 2026, 2:35 p.m.
NER Named-entity recognition batch_69f17820a8088190bc0ce907adf95863 completed April 29, 2026, 3:16 a.m.
Created at: April 17, 2026, 3:10 p.m.