Triple

T14183656
Position Surface form Disambiguated ID Type / Status
Subject Arsenal cinema Berlin E351519 entity
Predicate hasScreen P36451 FINISHED
Object Screen 2
Screen 2 is one of the individual auditoriums within the Arsenal art-house cinema in Berlin, used for film screenings and cultural events.
 E1084242 NE FINISHED

How this triple was built (4 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: Screen 2 | Statement: [Arsenal cinema Berlin, hasScreen, Screen 2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Screen 2
Context triple: [Arsenal cinema Berlin, hasScreen, Screen 2]
  • A. Line 2
    Line 2 is one of the main lines of the Santiago Metro in Chile, running in a generally north–south direction and serving several central and densely populated areas of the city.
  • B. Line 2
    Line 2 is a trolleybus route within Geneva’s public transport system that serves as one of the city’s main electric bus lines.
  • C. Line 2
    Line 2 is a rapid transit line of the Barcelona Metro system that serves several central and northern neighborhoods of the city.
  • D. Line 2
    Line 2 is a circular line of the Brussels Metro system that serves central and surrounding districts of the Belgian capital.
  • E. Line 2
    Line 2 is a major circular line of the Seoul Metropolitan Subway system, known for being one of the busiest and most important routes in the network.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Screen 2
Triple: [Arsenal cinema Berlin, hasScreen, Screen 2]
Generated description
Screen 2 is one of the individual auditoriums within the Arsenal art-house cinema in Berlin, used for film screenings and cultural events.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Screen 2
Target entity description: Screen 2 is one of the individual auditoriums within the Arsenal art-house cinema in Berlin, used for film screenings and cultural events.
  • A. Line 2
    Line 2 is one of the main lines of the Santiago Metro in Chile, running in a generally north–south direction and serving several central and densely populated areas of the city.
  • B. Line 2
    Line 2 is a trolleybus route within Geneva’s public transport system that serves as one of the city’s main electric bus lines.
  • C. Line 2
    Line 2 is a rapid transit line of the Barcelona Metro system that serves several central and northern neighborhoods of the city.
  • D. Line 2
    Line 2 is a circular line of the Brussels Metro system that serves central and surrounding districts of the Belgian capital.
  • E. Line 2
    Line 2 is a major circular line of the Seoul Metropolitan Subway system, known for being one of the busiest and most important routes in the network.
  • F. None of above. chosen

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_69d8278834a08190b0f1784e58d7b99c completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de61cc0a848190b660095972b1223b completed April 14, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcf81285c481908a5594bcb3304981 completed May 7, 2026, 8:37 p.m.
NEDg Description generation batch_69fd06a6e5d08190906cca66b2dcf565 completed May 7, 2026, 9:39 p.m.
NED2 Entity disambiguation (via description) batch_69fd07116e74819089aa9f75a11c6531 completed May 7, 2026, 9:41 p.m.
Created at: April 10, 2026, 1:03 a.m.