Triple

T6335939
Position Surface form Disambiguated ID Type / Status
Subject S9 E142490 entity
Predicate servesStation P839 FINISHED
Object Berlin Pankow E93479 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: Berlin Pankow | Statement: [S9, servesStation, Berlin Pankow]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Berlin Pankow
Context triple: [S9, servesStation, Berlin Pankow]
  • A. Pankow chosen
    Pankow is a northeastern borough of Berlin known for its mix of historic neighborhoods, green spaces, and the popular district of Prenzlauer Berg.
  • B. Reinickendorf
    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.
  • C. Berlin-Lichtenberg
    Berlin-Lichtenberg is a borough in eastern Berlin known for its mix of post-war residential areas, historical sites, and former East German administrative and security institutions.
  • D. 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.
  • E. Treptow-Köpenick
    Treptow-Köpenick is Berlin’s largest and greenest borough, known for its extensive forests, lakes, and historic town centers such as Köpenick.
  • 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_69c008d4d8e88190ad301c05b08722ac completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0654a88a881908d5cb2aa7f22c4c7 completed March 22, 2026, 9:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6d50184a081908286d92166fd1c00 completed March 27, 2026, 7:05 p.m.
Created at: March 22, 2026, 4:30 p.m.