Triple

T1676803
Position Surface form Disambiguated ID Type / Status
Subject GKN Sankey E36248 entity
Predicate parentCompany P254 FINISHED
Object GKN
GKN is a British multinational engineering company known for its aerospace, automotive, and powder metallurgy components.
 E36248 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: GKN | Statement: [GKN Sankey, parentCompany, GKN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: GKN
Context triple: [GKN Sankey, parentCompany, GKN]
  • A. GKN Sankey
    GKN Sankey was a British engineering and manufacturing company best known for producing military vehicles and automotive components.
  • B. Crompton
    Crompton is an English surname most notably associated with Samuel Crompton, the inventor of the spinning mule that revolutionized textile manufacturing during the Industrial Revolution.
  • C. Magna
    Magna is a suburban community in Salt Lake County, Utah, known for its historic roots in mining and its location near the southern shore of the Great Salt Lake.
  • D. Airco
    Airco was a British aircraft manufacturer best known for producing military aircraft during World War I, including the successful DH series of biplanes.
  • E. Krauss-Maffei Wegmann
    Krauss-Maffei Wegmann is a German defense company specializing in the design and production of armored vehicles and military land systems.
  • 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: GKN
Triple: [GKN Sankey, parentCompany, GKN]
Generated description
GKN is a British multinational engineering company known for its aerospace, automotive, and powder metallurgy components.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: GKN
Target entity description: GKN is a British multinational engineering company known for its aerospace, automotive, and powder metallurgy components.
  • A. GKN Sankey chosen
    GKN Sankey was a British engineering and manufacturing company best known for producing military vehicles and automotive components.
  • B. Crompton
    Crompton is an English surname most notably associated with Samuel Crompton, the inventor of the spinning mule that revolutionized textile manufacturing during the Industrial Revolution.
  • C. Magna
    Magna is a suburban community in Salt Lake County, Utah, known for its historic roots in mining and its location near the southern shore of the Great Salt Lake.
  • D. Airco
    Airco was a British aircraft manufacturer best known for producing military aircraft during World War I, including the successful DH series of biplanes.
  • E. Krauss-Maffei Wegmann
    Krauss-Maffei Wegmann is a German defense company specializing in the design and production of armored vehicles and military land systems.
  • F. None of above.

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_69a886139ed081909af0940aa9313512 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa625e4cd0819083825b41196f902d completed March 6, 2026, 5:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad71b7c2608190bce1dc7469da4c45 completed March 8, 2026, 12:55 p.m.
NEDg Description generation batch_69ad72deafcc8190a2aef87277c5d731 completed March 8, 2026, 1 p.m.
NED2 Entity disambiguation (via description) batch_69ad734440ac81909cbcf1c02fd4bc66 completed March 8, 2026, 1:01 p.m.
Created at: March 4, 2026, 7:29 p.m.