Triple

T1756687
Position Surface form Disambiguated ID Type / Status
Subject Bernhard E38564 entity
Predicate hasFeminineForm P1613 FINISHED
Object Bernharda
Bernharda is a feminine given name, primarily used in Central and Eastern Europe, derived from the masculine name Bernhard.
 E203640 NE FINISHED

How this triple was built (4 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: Bernharda | Statement: [Bernhard, hasFeminineForm, Bernharda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bernharda
Context triple: [Bernhard, hasFeminineForm, Bernharda]
  • A. Bernhard
    Bernhard is a male given name of Germanic origin, historically borne by various European nobles and royals, including Prince Bernhard of the Netherlands.
  • B. Othmar
    Othmar is a masculine given name of Germanic origin, notably borne by the Swiss-American civil engineer Othmar Ammann.
  • C. Franziska
    Franziska is a feminine given name of German origin, closely related to and cognate with the name Frances.
  • D. Gebhard
    Gebhard is a German given name most famously borne by Gebhard Leberecht von Blücher, the Prussian field marshal who helped defeat Napoleon at Waterloo.
  • E. Verena
    Verena is a feminine given name of Latin origin, commonly used in German-speaking and other European countries.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Bernharda
Triple: [Bernhard, hasFeminineForm, Bernharda]
Generated description
Bernharda is a feminine given name, primarily used in Central and Eastern Europe, derived from the masculine name Bernhard.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bernharda
Target entity description: Bernharda is a feminine given name, primarily used in Central and Eastern Europe, derived from the masculine name Bernhard.
  • A. Bernhard
    Bernhard is a male given name of Germanic origin, historically borne by various European nobles and royals, including Prince Bernhard of the Netherlands.
  • B. Othmar
    Othmar is a masculine given name of Germanic origin, notably borne by the Swiss-American civil engineer Othmar Ammann.
  • C. Franziska
    Franziska is a feminine given name of German origin, closely related to and cognate with the name Frances.
  • D. Gebhard
    Gebhard is a German given name most famously borne by Gebhard Leberecht von Blücher, the Prussian field marshal who helped defeat Napoleon at Waterloo.
  • E. Verena
    Verena is a feminine given name of Latin origin, commonly used in German-speaking and other European countries.
  • F. None of above. chosen

Provenance (5 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_69a8862bdb2081908aefe831c8aa8017 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa643ce88481909d2feef3c5fd849f completed March 6, 2026, 5:21 a.m.
NED1 Entity disambiguation (via context triple) batch_69adbf4db1c48190a96f137db3e2f32c completed March 8, 2026, 6:26 p.m.
NEDg Description generation batch_69adc0a3fdd88190b0ffa98db1b5cf80 completed March 8, 2026, 6:32 p.m.
NED2 Entity disambiguation (via description) batch_69adc12c894881909c9a82fc9e363a41 completed March 8, 2026, 6:34 p.m.
Created at: March 4, 2026, 7:31 p.m.