Triple

T6335820
Position Surface form Disambiguated ID Type / Status
Subject S41 E142487 entity
Predicate connectsDistrict P2564 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: [S41, connectsDistrict, Neukölln]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Neukölln
Context triple: [S41, connectsDistrict, 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_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_69c669d97d348190bca19013edbf436c completed March 27, 2026, 11:28 a.m.
Created at: March 22, 2026, 4:30 p.m.