Triple

T3706954
Position Surface form Disambiguated ID Type / Status
Subject Gottlieb Daimler E80915 entity
Predicate employer P7 FINISHED
Object Deutz AG E318785 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: Deutz AG | Statement: [Gottlieb Daimler, employer, Deutz AG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Deutz AG
Context triple: [Gottlieb Daimler, employer, Deutz AG]
  • A. Deutz AG chosen
    Deutz AG is a German manufacturer best known for producing internal combustion engines, particularly for industrial and agricultural applications.
  • B. Krauss-Maffei Wegmann
    Krauss-Maffei Wegmann is a German defense company specializing in the design and production of armored vehicles and military land systems.
  • C. Borsigwerke
    Borsigwerke is a Berlin U-Bahn station on line U6 serving the Tegel district in the city’s northwest.
  • D. Voith
    Voith is a German multinational engineering company known for its technologies and services in sectors such as energy, paper, raw materials, and transportation.
  • E. Steyr-Daimler-Puch
    Steyr-Daimler-Puch was a major Austrian industrial conglomerate best known for producing firearms, vehicles, and machinery throughout the 20th century.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ad8b1793888190a5f70e4b21dc05a1 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adc57edd748190a006e15fa0248679 completed March 8, 2026, 6:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4cdfe77a481908880d0a4a5946656 completed March 14, 2026, 2:54 a.m.
Created at: March 8, 2026, 3:33 p.m.