Triple

T21735536
Position Surface form Disambiguated ID Type / Status
Subject 112 (album) E536511 entity
Predicate producer P490 FINISHED
Object Arnold Hennings
Arnold Hennings is a music producer best known for his work in R&B and hip hop, including collaborations with prominent artists in the 1990s and 2000s.
E1499667 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: Arnold Hennings | Statement: [112 (album), producer, Arnold Hennings]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Arnold Hennings
Context triple: [112 (album), producer, Arnold Hennings]
  • A. Arnold Eidus
    Arnold Eidus was an American classical violinist and recording artist known for his virtuosity and work as a soloist and studio musician in the mid-20th century.
  • B. Hans Dreier
    Hans Dreier was a prominent German-born art director in Hollywood’s Golden Age, best known for his influential visual design work on numerous Paramount Pictures films.
  • C. Arne Hanna
    Arne Hanna is a musician best known as a member of the Australian ambient/world music band Not Drowning, Waving.
  • D. George Hansen
    George Hansen is a fictional character from the 1958 Western film "Terror in a Texas Town."
  • E. George Hansen
    George Hansen is a relatively common personal name that may refer to multiple individuals across different fields, such as politics, sports, or academia.
  • 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: Arnold Hennings
Triple: [112 (album), producer, Arnold Hennings]
Generated description
Arnold Hennings is a music producer best known for his work in R&B and hip hop, including collaborations with prominent artists in the 1990s and 2000s.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Arnold Hennings
Target entity description: Arnold Hennings is a music producer best known for his work in R&B and hip hop, including collaborations with prominent artists in the 1990s and 2000s.
  • A. Arnold Eidus
    Arnold Eidus was an American classical violinist and recording artist known for his virtuosity and work as a soloist and studio musician in the mid-20th century.
  • B. Hans Dreier
    Hans Dreier was a prominent German-born art director in Hollywood’s Golden Age, best known for his influential visual design work on numerous Paramount Pictures films.
  • C. Arne Hanna
    Arne Hanna is a musician best known as a member of the Australian ambient/world music band Not Drowning, Waving.
  • D. George Hansen
    George Hansen is a fictional character from the 1958 Western film "Terror in a Texas Town."
  • E. George Hansen
    George Hansen is a relatively common personal name that may refer to multiple individuals across different fields, such as politics, sports, or academia.
  • 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_69e0c46d3284819099a4f9d5a704eb95 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69effd0b0da0819098ef03360eea6a0d completed April 28, 2026, 12:19 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0a2ed25d808190bf6afff41d8812d4 completed May 17, 2026, 9:10 p.m.
NEDg Description generation batch_6a0a2ffaaa708190b43531cbc6448b00 completed May 17, 2026, 9:15 p.m.
NED2 Entity disambiguation (via description) batch_6a0a307b81288190a8254e32b36ed3f1 completed May 17, 2026, 9:17 p.m.
Created at: April 16, 2026, 6:48 p.m.