Triple

T4909441
Position Surface form Disambiguated ID Type / Status
Subject Rineke Dijkstra E110195 entity
Predicate notableWork P4 FINISHED
Object Tiergarten series E106564 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: Tiergarten series | Statement: [Rineke Dijkstra, notableWork, Tiergarten series]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tiergarten series
Context triple: [Rineke Dijkstra, notableWork, Tiergarten series]
  • A. Kaiserwiese
    Kaiserwiese is a large open meadow within Vienna’s Prater park, commonly used for recreation, events, and public gatherings.
  • B. 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.
  • C. Tiergarten chosen
    Tiergarten is a large central park in Berlin known for its expansive green spaces, monuments, and cultural landmarks.
  • D. Unter den Linden
    Unter den Linden is a historic and grand boulevard in central Berlin, Germany, renowned for its cultural institutions, landmarks, and role as a major ceremonial avenue.
  • E. Neuer Garten
    Neuer Garten is a historic landscaped park in Potsdam, Germany, known for its picturesque lakeside setting and palaces such as Cecilienhof and Marmorpalais.
  • 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_69bd44132b94819088522d92beaadc78 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6e99414081908c3d3283f563bba4 completed March 20, 2026, 3:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69be6fe43a888190ab1b150da0f49203 completed March 21, 2026, 10:16 a.m.
Created at: March 20, 2026, 1:29 p.m.