Triple

T7998632
Position Surface form Disambiguated ID Type / Status
Subject Alexanderplatz station E186189 entity
Predicate locatedIn P40 FINISHED
Object Alexanderplatz E136235 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: Alexanderplatz | Statement: [Alexanderplatz station, locatedIn, Alexanderplatz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alexanderplatz
Context triple: [Alexanderplatz station, locatedIn, Alexanderplatz]
  • A. Alexanderplatz chosen
    Alexanderplatz is a major public square and transport hub in central Berlin, known for its historic role in the city’s social and political life and its surrounding modernist architecture.
  • B. Marienplatz
    Marienplatz is the central square in Munich, Germany, renowned as the city's historic heart and a major hub for cultural events, tourism, and public life.
  • C. Leipziger Platz
    Leipziger Platz is a historic square in central Berlin, Germany, known for its reconstruction after German reunification and its proximity to Potsdamer Platz.
  • D. Luisenplatz
    Luisenplatz is the central square and main public plaza of Darmstadt, Germany, serving as a key hub for transportation, shopping, and civic life.
  • E. Fehrbelliner Platz
    Fehrbelliner Platz is a Berlin U-Bahn station in the Wilmersdorf district that serves as a key transfer point between multiple subway lines.
  • 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_69ca82aaaf24819084b94d18f699ba53 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3c9a12788190a5607a538f4e07c1 completed March 31, 2026, 3:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69ccecaf89e88190b347abc200d1a266 completed April 1, 2026, 10 a.m.
Created at: March 30, 2026, 5:17 p.m.