Triple

T14835335
Position Surface form Disambiguated ID Type / Status
Subject Alsergrund E348815 entity
Predicate hasNeighbouringDistrict P17964 FINISHED
Object Margareten E346712 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: Margareten | Statement: [Alsergrund, hasNeighbouringDistrict, Margareten]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Margareten
Context triple: [Alsergrund, hasNeighbouringDistrict, Margareten]
  • A. Margareten chosen
    Margareten is the 5th district of Vienna, Austria, known as a densely populated, traditionally working-class area that has undergone significant urban renewal and gentrification.
  • B. St. Margrethen
    St. Margrethen is a municipality in the canton of St. Gallen in northeastern Switzerland, located near the Austrian border along the Rhine River.
  • C. Magdelon
    Magdelon is one of the two naive, pretentious young women satirized for their affected manners and romantic fantasies in Molière’s comedy *Les Précieuses ridicules*.
  • D. Leitha
    Leitha is a river in Central Europe that flows through Austria and Hungary and serves as part of the historical border between the two countries.
  • E. Veleslavín
    Veleslavín is a residential district in the northwestern part of Prague known for its transport connections and proximity to green areas.
  • 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_69d822ec69008190a9232caa68836872 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded076ac9c8190a05cabec5e87d207 completed April 14, 2026, 11:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe38a5d8888190821988ad00351d05 completed May 8, 2026, 7:25 p.m.
Created at: April 10, 2026, 1:52 a.m.