Triple

T13036077
Position Surface form Disambiguated ID Type / Status
Subject 42 (school) E326563 entity
Predicate hasCampus P116 FINISHED
Object 42 Berlin
42 Berlin is a tuition-free, peer-to-peer coding school in Berlin that is part of the international 42 network focused on project-based software engineering education.
 E1017935 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: 42 Berlin | Statement: [42 (school), hasCampus, 42 Berlin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: 42 Berlin
Context triple: [42 (school), hasCampus, 42 Berlin]
  • A. New Berlin
    New Berlin is a suburban city in Waukesha County, Wisconsin, located just west of Milwaukee and known for its residential communities and light industry.
  • B. New Berlin
    New Berlin is a small village in Sangamon County, Illinois, located near Springfield and known for its rural Midwestern character.
  • C. Berliner
    Berliner is a German-origin surname most notably associated with Emile Berliner, the inventor of the gramophone and a pioneer in sound recording technology.
  • D. Berlin B
    Berlin B is one of the public transport fare zones in Berlin, covering the outer areas of the city beyond the central A zone.
  • E. Leverkusen
    Leverkusen is a city in western Germany, known for its chemical industry and as the home of the football club Bayer 04 Leverkusen.
  • 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: 42 Berlin
Triple: [42 (school), hasCampus, 42 Berlin]
Generated description
42 Berlin is a tuition-free, peer-to-peer coding school in Berlin that is part of the international 42 network focused on project-based software engineering education.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: 42 Berlin
Target entity description: 42 Berlin is a tuition-free, peer-to-peer coding school in Berlin that is part of the international 42 network focused on project-based software engineering education.
  • A. New Berlin
    New Berlin is a suburban city in Waukesha County, Wisconsin, located just west of Milwaukee and known for its residential communities and light industry.
  • B. New Berlin
    New Berlin is a small village in Sangamon County, Illinois, located near Springfield and known for its rural Midwestern character.
  • C. Berliner
    Berliner is a German-origin surname most notably associated with Emile Berliner, the inventor of the gramophone and a pioneer in sound recording technology.
  • D. Berlin B
    Berlin B is one of the public transport fare zones in Berlin, covering the outer areas of the city beyond the central A zone.
  • E. Leverkusen
    Leverkusen is a city in western Germany, known for its chemical industry and as the home of the football club Bayer 04 Leverkusen.
  • 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_69d8076cc45c81908123123f43e69266 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97f2a71a0819098bb6cf8a4b2208a completed April 10, 2026, 10:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6cbcf11f88190ab1746f973132af1 completed May 3, 2026, 4:15 a.m.
NEDg Description generation batch_69f6cee0a27081909203e3331186b4ca completed May 3, 2026, 4:28 a.m.
NED2 Entity disambiguation (via description) batch_69f6cf987f68819084edcd6613832fe8 completed May 3, 2026, 4:31 a.m.
Created at: April 9, 2026, 8:55 p.m.