Triple

T7766905
Position Surface form Disambiguated ID Type / Status
Subject Kiev Infantry Junker School E176171 entity
Predicate location P40 FINISHED
Object Kiev E17733 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: Kiev | Statement: [Kiev Infantry Junker School, location, Kiev]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kiev
Context triple: [Kiev Infantry Junker School, location, Kiev]
  • A. Kyiv chosen
    Kyiv is the capital and largest city of Ukraine, serving as its political, cultural, and economic center.
  • B. Kharkiv
    Kharkiv is Ukraine’s second-largest city and a major industrial, cultural, and educational center in the northeast of the country.
  • C. Dnipro
    Dnipro is one of Ukraine’s largest industrial and cultural centers, located on the Dnieper River in the central-eastern part of the country.
  • D. Odessa
    Odessa is a mid-sized city in western Texas known for its oil industry, high school football culture, and role in the Permian Basin energy region.
  • E. Odessa
    Odessa is a central, devoutly religious housekeeper in James Baldwin’s play "The Amen Corner," known for her loyalty and moral grounding amid the story’s family and church conflicts.
  • 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_69c69962923c8190ac74d28b4f9fe0a0 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c7043451bc8190a76ee066b779b7d7 completed March 27, 2026, 10:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8d6d0f1988190b4df650bebb61fd3 completed March 29, 2026, 7:37 a.m.
Created at: March 27, 2026, 4:10 p.m.