Triple

T1985671
Position Surface form Disambiguated ID Type / Status
Subject Faculty of Science and Technology, University of Tartu E43133 entity
Predicate locatedIn P40 FINISHED
Object Tartu County E222912 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: Tartu County | Statement: [Faculty of Science and Technology, University of Tartu, locatedIn, Tartu County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tartu County
Context triple: [Faculty of Science and Technology, University of Tartu, locatedIn, Tartu County]
  • A. Tartu County chosen
    Tartu County is an administrative region in eastern Estonia centered around the university city of Tartu and known for its cultural, educational, and economic significance.
  • B. Harku Parish
    Harku Parish is a rural municipality in northern Estonia, located just west of the capital city Tallinn.
  • C. Viljandi
    Viljandi is a historic town in southern Estonia known for its medieval castle ruins, rich cultural life, and annual folk music festival.
  • D. Suuremõisa
    Suuremõisa is a village on the Estonian island of Hiiumaa, known for its historic manor complex and surrounding park.
  • E. Kuressaare
    Kuressaare is the main town on Estonia’s Saaremaa island, known for its well-preserved medieval castle and seaside spa resort atmosphere.
  • 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_69a88713ddc88190a969715658ebe7a8 completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb8232d788190938f261fd4b2f2fd completed March 7, 2026, 5:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae2705752c81908054e8e0e426e86d completed March 9, 2026, 1:48 a.m.
Created at: March 4, 2026, 7:37 p.m.