Triple

T23227270
Position Surface form Disambiguated ID Type / Status
Subject Luisa Miller E581047 entity
Predicate settingCountry P838 FINISHED
Object Tyrol 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: Tyrol | Statement: [Luisa Miller, settingCountry, Tyrol]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tyrol
Context triple: [Luisa Miller, settingCountry, Tyrol]
  • A. Tyrol chosen
    Tyrol is a mountainous federal state in western Austria, renowned for its Alpine landscapes, ski resorts, and hiking regions.
  • B. East Tyrol
    East Tyrol is a mountainous district in the Austrian state of Tyrol, known for its Alpine landscapes, hiking and skiing areas, and relatively sparse population.
  • C. Western Tyrol
    Western Tyrol is a mountainous region in western Austria known for its high Alpine peaks, including the Wildspitze, and popular ski and hiking areas.
  • D. Tirole
    Tirole is the surname of Jean Tirole, a prominent French economist and Nobel laureate known for his work on industrial organization and regulation.
  • E. Carinthia
    Carinthia is a mountainous federal state in southern Austria known for its Alpine landscapes, lakes, and outdoor tourism.
  • 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_69e246043c48819089bae72c9a9c306c completed April 17, 2026, 2:39 p.m.
NER Named-entity recognition batch_69f1922f5b4081908145d66ea7534493 completed April 29, 2026, 5:07 a.m.
Created at: April 17, 2026, 4:09 p.m.