Triple

T10423769
Position Surface form Disambiguated ID Type / Status
Subject Hummel E245731 entity
Predicate manufacturer P490 FINISHED
Object Deutsche Eisenwerke E862505 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: Deutsche Eisenwerke | Statement: [Hummel, manufacturer, Deutsche Eisenwerke]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Deutsche Eisenwerke
Context triple: [Hummel, manufacturer, Deutsche Eisenwerke]
  • A. Deutsche Eisenwerke chosen
    Deutsche Eisenwerke was a German industrial firm known for producing military equipment, including armored vehicles, during the World War II era.
  • B. Preussag AG
    Preussag AG was a former German industrial and mining conglomerate that transformed in the 1990s into a tourism-focused company, eventually becoming today’s TUI Group.
  • C. Borsigwerke
    Borsigwerke is a Berlin U-Bahn station on line U6 serving the Tegel district in the city’s northwest.
  • D. Krupp (company)
    Krupp (company) was a major German industrial conglomerate best known for its steel production and armaments manufacturing, playing a central role in both World Wars and in the development of heavy industry in Germany.
  • E. ThyssenKrupp AG
    ThyssenKrupp AG is a major German multinational conglomerate specializing in industrial engineering and steel production, with significant operations in areas such as elevators, automotive components, and plant technology.
  • 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_69d381bf3dc08190bf35a2643e4e8f22 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4ea2de4d48190aee65b3f6ec3cc48 completed April 7, 2026, 11:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69d87ea554888190bf2ef31e33c0ff14 completed April 10, 2026, 4:37 a.m.
Created at: April 6, 2026, 12:12 p.m.