Triple

T15736274
Position Surface form Disambiguated ID Type / Status
Subject Tempelhof E381479 entity
Predicate adjacentTo P224 FINISHED
Object Neukölln E155228 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: Neukölln | Statement: [Tempelhof, adjacentTo, Neukölln]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Neukölln
Context triple: [Tempelhof, adjacentTo, Neukölln]
  • A. Neukölln chosen
    Neukölln is a diverse, historically working-class district in southern Berlin known for its vibrant multicultural community, nightlife, and rapidly changing urban landscape.
  • B. Friedrichshain
    Friedrichshain is a vibrant district in Berlin known for its alternative culture, nightlife, and historic sites including remnants of the Berlin Wall.
  • C. 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.
  • D. Tempelhof-Schöneberg
    Tempelhof-Schöneberg is a borough of Berlin, Germany, known for its mix of historic residential areas, the former Tempelhof Airport, and significant Cold War-era political sites.
  • 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_69d86d9cdb648190bf3171be0bd7d872 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e04fd586a88190aa1b1b88368d386f completed April 16, 2026, 2:56 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffb59779788190a393237f5293fe8d completed May 9, 2026, 10:30 p.m.
Created at: April 10, 2026, 4:46 a.m.