Triple

T3848789
Position Surface form Disambiguated ID Type / Status
Subject Berlin U-Bahn line U4 E85238 entity
Predicate openedBy P421 FINISHED
Object City of Schöneberg E13289 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: City of Schöneberg | Statement: [Berlin U-Bahn line U4, openedBy, City of Schöneberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Schöneberg
Context triple: [Berlin U-Bahn line U4, openedBy, City of Schöneberg]
  • A. Dorotheenstadt
    Dorotheenstadt is a historic district in central Berlin, Germany, known for its cultural significance and notable institutions.
  • B. Oststadt
    Oststadt is a central district of Hanover, Germany, known for its urban residential areas, cultural venues, and proximity to the city’s main commercial and administrative centers.
  • C. Bad Godesberg
    Bad Godesberg is a district in the city of Bonn, Germany, known for its affluent residential areas, former diplomatic missions, and scenic location along the Rhine River.
  • D. Schöneberg chosen
    Schöneberg is a district of Berlin, Germany, historically notable as the site of John F. Kennedy’s famous “Ich bin ein Berliner” speech.
  • E. Sorpedamm
    Sorpedamm is a reservoir dam in North Rhine-Westphalia, Germany, primarily used for water supply, flood control, and recreation.
  • 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_69aed936de1c81908f91bed80f70abb2 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeebcde86081908cf3840ae002acfa completed March 9, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69b504172d58819089d19f5cb7b803ec completed March 14, 2026, 6:45 a.m.
Created at: March 9, 2026, 3:19 p.m.