Triple

T36912931
Position Surface form Disambiguated ID Type / Status
Subject Breitscheidplatz E912961 entity
Predicate hasLandmark P105 FINISHED
Object Zoo Palast cinema
Zoo Palast cinema is a historic and iconic movie theater in Berlin, renowned for hosting major film premieres and events, including screenings during the Berlin International Film Festival.
 E2204240 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: Zoo Palast cinema | Statement: [Breitscheidplatz, hasLandmark, Zoo Palast cinema]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Zoo Palast cinema
Triple: [Breitscheidplatz, hasLandmark, Zoo Palast cinema]
Generated description
Zoo Palast cinema is a historic and iconic movie theater in Berlin, renowned for hosting major film premieres and events, including screenings during the Berlin International Film Festival.

Provenance (5 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_69f76e879768819085c2fb31a6a5b44b completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f9fdb21968819094da81a6bc92e5fa completed May 5, 2026, 2:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3e1629fb688190bcdbae073881dfb0 completed June 26, 2026, 6:03 a.m.
NEDg Description generation batch_6a3e16a50d8c819094deb898cab90904 completed June 26, 2026, 6:05 a.m.
NED2 Entity disambiguation (via description) batch_6a3e1b4f74f48190b12de0f00e7ab8b9 completed June 26, 2026, 6:25 a.m.
Created at: May 3, 2026, 4:13 p.m.