Triple

T11717593
Position Surface form Disambiguated ID Type / Status
Subject Henk Kummeling E278541 entity
Predicate givenName P17 FINISHED
Object Henk
Henk is a Dutch male given name commonly used in the Netherlands, often as a short form of Hendrik.
 E942733 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: Henk | Statement: [Henk Kummeling, givenName, Henk]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Henk
Context triple: [Henk Kummeling, givenName, Henk]
  • A. Hein
    Hein is a Dutch surname most notably borne by Piet Hein, a renowned 17th-century naval officer and folk hero of the Dutch Republic.
  • B. Henkin
    Henkin is a surname most notably associated with Leon Henkin, an influential logician known for his work in the foundations of mathematics and completeness in first-order logic.
  • C. Hannen
    Hannen is an English surname associated with several notable figures, including actors and judges, in British history.
  • D. Han
    Han is a common transliteration of the historical Central Asian title "Khan," often associated with rulers and nobility in various Turkic and Mongolic cultures.
  • E. Han
    Han refers to the majority ethnic group in China, historically associated with Chinese civilization, language, and culture.
  • 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: Henk
Triple: [Henk Kummeling, givenName, Henk]
Generated description
Henk is a Dutch male given name commonly used in the Netherlands, often as a short form of Hendrik.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Henk
Target entity description: Henk is a Dutch male given name commonly used in the Netherlands, often as a short form of Hendrik.
  • A. Hein
    Hein is a Dutch surname most notably borne by Piet Hein, a renowned 17th-century naval officer and folk hero of the Dutch Republic.
  • B. Henkin
    Henkin is a surname most notably associated with Leon Henkin, an influential logician known for his work in the foundations of mathematics and completeness in first-order logic.
  • C. Hannen
    Hannen is an English surname associated with several notable figures, including actors and judges, in British history.
  • D. Han
    Han is a common transliteration of the historical Central Asian title "Khan," often associated with rulers and nobility in various Turkic and Mongolic cultures.
  • E. Han
    Han refers to the majority ethnic group in China, historically associated with Chinese civilization, language, and culture.
  • 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_69d6aaff2ce88190b4a1e4b341ad5377 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a4c10d988190842acd824135cf15 completed April 10, 2026, 7:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69ef83a9479c81909cbe63d81255a1bf completed April 27, 2026, 3:41 p.m.
NEDg Description generation batch_69ef96b13be881908102ffa867f96c22 completed April 27, 2026, 5:02 p.m.
NED2 Entity disambiguation (via description) batch_69efb51113708190998b570c33b9d0e7 completed April 27, 2026, 7:12 p.m.
Created at: April 8, 2026, 9:40 p.m.