Triple

T9985358
Position Surface form Disambiguated ID Type / Status
Subject Erich Gutenberg E196553 entity
Predicate familyName P18 FINISHED
Object Gutenberg E196553 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: Gutenberg | Statement: [Erich Gutenberg, familyName, Gutenberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gutenberg
Context triple: [Erich Gutenberg, familyName, Gutenberg]
  • A. Gutenberg
    Gutenberg is the block-based content editor introduced in WordPress to enable more flexible, visual page and post creation.
  • B. Guttenberg
    Guttenberg is a surname most famously associated with American actor and comedian Steve Guttenberg, known for his roles in 1980s films such as the Police Academy series.
  • C. Gutenberg the Geek
    Gutenberg the Geek is a book by media theorist Jeff Jarvis that reinterprets Johannes Gutenberg as an early tech entrepreneur to draw parallels between the printing revolution and the digital age.
  • D. Erich Gutenberg chosen
    Erich Gutenberg was a prominent German economist and business administration scholar known for fundamentally shaping modern German management theory and production economics.
  • E. Johannes Gutenberg
    Johannes Gutenberg was a 15th-century German inventor and printer credited with introducing movable-type printing to Europe, revolutionizing the spread of information.
  • 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_69ca82efbce081908179b4b9c65096eb completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdb8bf5adc81908c862b75053dd8f1 completed April 2, 2026, 12:30 a.m.
NED1 Entity disambiguation (via context triple) batch_69d257fe0e348190b55fbd38e21cff7c completed April 5, 2026, 12:39 p.m.
Created at: March 30, 2026, 8:49 p.m.