Triple

T16182108
Position Surface form Disambiguated ID Type / Status
Subject Märkisches Viertel E392708 entity
Predicate locatedIn P40 FINISHED
Object Reinickendorf E14596 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: Reinickendorf | Statement: [Märkisches Viertel, locatedIn, Reinickendorf]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Reinickendorf
Context triple: [Märkisches Viertel, locatedIn, Reinickendorf]
  • A. Reinickendorf chosen
    Reinickendorf is a borough in the northwest of Berlin, Germany, known for its mix of residential neighborhoods, industrial areas, and green spaces including parts of Lake Tegel.
  • B. Pankow
    Pankow is a northeastern borough of Berlin known for its mix of historic neighborhoods, green spaces, and the popular district of Prenzlauer Berg.
  • C. Treptow-Köpenick
    Treptow-Köpenick is Berlin’s largest and greenest borough, known for its extensive forests, lakes, and historic town centers such as Köpenick.
  • D. Neukölln
    Neukölln is a diverse, historically working-class district in southern Berlin known for its vibrant multicultural community, nightlife, and rapidly changing urban landscape.
  • E. Steglitz-Zehlendorf
    Steglitz-Zehlendorf is a borough in southwestern Berlin known for its affluent residential areas, lakes and forests, and historically significant sites such as the Wannsee Conference villa.
  • 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_69d87f1e49ac8190a311b54d32990576 completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e2205d858c8190802d44e08e3cdcd6 completed April 17, 2026, 11:58 a.m.
NED1 Entity disambiguation (via context triple) batch_6a008a1f8d648190b9c6280b875a17e4 completed May 10, 2026, 1:37 p.m.
Created at: April 10, 2026, 5:02 a.m.