Triple

T14472897
Position Surface form Disambiguated ID Type / Status
Subject Janis E358888 entity
Predicate hasEtymologicalRoot P5801 FINISHED
Object Johannes E551148 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: Johannes | Statement: [Janis, hasEtymologicalRoot, Johannes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Johannes
Context triple: [Janis, hasEtymologicalRoot, Johannes]
  • A. 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.
  • B. Johannes chosen
    Johannes is a masculine given name of Hebrew origin, related to names like John and Johan and common in various European languages.
  • C. Johannes
    Johannes is the given first name of Paul Kruger, the prominent 19th-century Boer leader and president of the South African Republic.
  • D. Johannes
    Johannes is the given name of Frederik Johannes Willem Reitz, a prominent South African lawyer, politician, and former State President of the Orange Free State.
  • 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 (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_69d827966698819082e140837737501d completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de91fab21c819090b6e209d8efba6e completed April 14, 2026, 7:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd649e103c81908001b45c16d1fd79 completed May 8, 2026, 4:20 a.m.
Created at: April 10, 2026, 1:20 a.m.