Triple

T7368088
Position Surface form Disambiguated ID Type / Status
Subject Shepparton E169920 entity
Predicate near P350 FINISHED
Object Tatura E637033 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: Tatura | Statement: [Shepparton, near, Tatura]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tatura
Context triple: [Shepparton, near, Tatura]
  • A. Tatura chosen
    Tatura is a rural town in northern Victoria, Australia, known for its agricultural industries and historical World War II internment camps.
  • B. Osek
    Osek is a town in the Czech Republic historically associated with the family origins of writer Franz Kafka’s father, Hermann Kafka.
  • C. Beroun
    Beroun is a historic town in the Czech Republic known for its medieval architecture, scenic location at the confluence of the Berounka and Litavka rivers, and proximity to the Bohemian Karst nature reserve.
  • D. Lučenec
    Lučenec is a town in southern Slovakia known as a regional center of trade, transport, and culture in the Novohrad area.
  • E. Švihov
    Švihov is a small town in the Plzeň Region of the Czech Republic, known for its well-preserved water castle and its location on the Úhlava River.
  • 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_69c68a5ade988190885b7175f63b7534 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f17fe278819094eb1dd886583c6a completed March 27, 2026, 9:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69c802bfaa10819083ab3137dbdeefb6 completed March 28, 2026, 4:33 p.m.
Created at: March 27, 2026, 3:07 p.m.