Triple

T14405355
Position Surface form Disambiguated ID Type / Status
Subject U-Bahnhof Seestraße E357181 entity
Predicate intersectionWith P13379 FINISHED
Object Müllerstraße E376992 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: Müllerstraße | Statement: [U-Bahnhof Seestraße, intersectionWith, Müllerstraße]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Müllerstraße
Context triple: [U-Bahnhof Seestraße, intersectionWith, Müllerstraße]
  • A. Müllerstraße chosen
    Müllerstraße is a major thoroughfare in Berlin’s Wedding district, known for its dense urban character, shops, and public transport connections.
  • B. Siesmayerstraße
    Siesmayerstraße is a street in Frankfurt am Main, Germany, known for bordering the historic Palmengarten botanical garden.
  • C. Güntzelstraße
    Güntzelstraße is a Berlin U-Bahn station on line U9 located in the Wilmersdorf district of the city.
  • D. Hedderichstraße
    Hedderichstraße is a street in Frankfurt am Main, Germany, located in the Sachsenhausen district and connected to the city’s public transport network.
  • E. Grunerstraße
    Grunerstraße is a central street in Berlin located near Alexanderplatz, known for carrying heavy traffic through the city’s Mitte district.
  • 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_69d82793421c8190861eb0e673b085de completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de908804048190a4fe58afc2e0a5b6 completed April 14, 2026, 7:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69febfcd67f081909f97bcf38d814a13 completed May 9, 2026, 5:02 a.m.
Created at: April 10, 2026, 1:17 a.m.