Triple

T21111979
Position Surface form Disambiguated ID Type / Status
Subject Alar Karis E520193 entity
Predicate workLocation P7 FINISHED
Object Tartu 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: Tartu | Statement: [Alar Karis, workLocation, Tartu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tartu
Context triple: [Alar Karis, workLocation, Tartu]
  • A. Tartu chosen
    Tartu is Estonia’s second-largest city and a historic cultural and intellectual center, best known as the country’s main university town.
  • B. Kohtla-Järve
    Kohtla-Järve is an industrial city in northeastern Estonia known for its oil shale industry and diverse population.
  • C. Tallinn
    Tallinn is the capital and largest city of Estonia, a historic Baltic Sea port known for its well-preserved medieval Old Town and strategic maritime location.
  • D. Haapsalu
    Haapsalu is a small seaside town in western Estonia known for its historic wooden architecture, medieval castle, and traditional seaside resort and spa culture.
  • E. Jõgeva
    Jõgeva is a small town in eastern Estonia known as a local administrative and cultural center and for recording some of the country’s lowest winter temperatures.
  • 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_69e0b509a318819092fbbcb21d1fe603 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e72102d99c8190a1ea5a6981da6da0 completed April 21, 2026, 7:02 a.m.
Created at: April 16, 2026, 2:54 p.m.