Triple

T22297095
Position Surface form Disambiguated ID Type / Status
Subject Johannes E551148 entity
Predicate isRelatedTo P37 FINISHED
Object Johann NE NERFINISHED

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: Johann | Statement: [Johannes, isRelatedTo, Johann]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Johann
Context triple: [Johannes, isRelatedTo, Johann]
  • A. Johann chosen
    Johann is a given name of Germanic origin commonly used in German-speaking and other European countries.
  • B. Johannes
    Johannes is the given first name of Hubertus van Mook, a Dutch colonial administrator who served as Governor-General of the Dutch East Indies during and after World War II.
  • C. Johannes
    Johannes is a masculine given name of Hebrew origin, related to names like John and Johan and common in various European languages.
  • D. Johannes
    Johannes is the given first name of Paul Kruger, the prominent 19th-century Boer leader and president of the South African Republic.
  • E. Johannes
    Johannes is the given first name of the German nuclear physicist Hans D. Jensen, a Nobel Prize laureate in Physics.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e45fb848190a1b2ae21296e3a5f completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15720fba0819080f6c96f6df4f1e0 completed April 29, 2026, 12:56 a.m.
Created at: April 16, 2026, 8:41 p.m.