Triple

T8389526
Position Surface form Disambiguated ID Type / Status
Subject Semperoper E197905 entity
Predicate hasCityDistrict P2709 FINISHED
Object Altstadt E197907 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: Altstadt | Statement: [Semperoper, hasCityDistrict, Altstadt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Altstadt
Context triple: [Semperoper, hasCityDistrict, Altstadt]
  • A. Altstadt chosen
    Altstadt is the historic old town district of Dresden, Germany, known for its baroque architecture and major cultural landmarks.
  • B. Altstadt
    Altstadt is the historic old town of Zürich, Switzerland, known for its medieval streets, preserved architecture, and cultural landmarks along the Limmat River.
  • C. Altstadt
    Altstadt is the historic old town district of Koblenz, Germany, known for its preserved medieval streets, squares, and traditional architecture along the Rhine and Moselle rivers.
  • D. Altstadt
    Altstadt is the historic old town of Salzburg, Austria, renowned for its well-preserved baroque architecture and status as a UNESCO World Heritage Site.
  • E. Altstadt
    Altstadt is the historic old town of Düsseldorf, Germany, known for its dense concentration of bars, traditional breweries, and cultural landmarks along the Rhine 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_69ca82f749388190bffbea6dfb509016 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb810ac380819095bd67f0555ac2a8 completed March 31, 2026, 8:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69cde84427dc8190925150b5d52bc9a0 completed April 2, 2026, 3:53 a.m.
Created at: March 30, 2026, 6:03 p.m.