Triple

T669951
Position Surface form Disambiguated ID Type / Status
Subject German Archaeological Institute E12948 entity
Predicate hasOffice P1268 FINISHED
Object Ulaanbaatar E24958 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: Ulaanbaatar | Statement: [German Archaeological Institute, hasOffice, Ulaanbaatar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ulaanbaatar
Context triple: [German Archaeological Institute, hasOffice, Ulaanbaatar]
  • A. Ulaanbaatar chosen
    Ulaanbaatar is the capital and largest city of Mongolia, serving as its political, economic, and cultural center.
  • B. Hohhot
    Hohhot is the capital and largest city of Inner Mongolia in northern China, known as a regional center of politics, culture, and industry.
  • C. Almaty
    Almaty is the largest city and main commercial and cultural center of Kazakhstan, located in the country’s mountainous southeast.
  • D. Shymkent
    Shymkent is one of the largest and most populous cities in southern Kazakhstan, serving as a key industrial, commercial, and cultural center of the region.
  • E. Astana
    Astana is the planned, modernist capital city of Kazakhstan, known for its futuristic architecture and rapid development since the late 20th century.
  • 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_69a493355dec819098d4244b2fa34885 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49ffd2b508190ac5adc04163e360f completed March 1, 2026, 8:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5c39d11508190a3bd0f118d122e1a completed March 2, 2026, 5:06 p.m.
Created at: March 1, 2026, 7:36 p.m.