Triple

T8644016
Position Surface form Disambiguated ID Type / Status
Subject Houben E204726 entity
Predicate hasNotableBearer P458 FINISHED
Object Katrin Houben
Katrin Houben is an individual notable enough to be recognized as a namesake or prominent bearer of the surname Houben.
E762785 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: Katrin Houben | Statement: [Houben, hasNotableBearer, Katrin Houben]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Katrin Houben
Context triple: [Houben, hasNotableBearer, Katrin Houben]
  • A. Sabine Völker
    Sabine Völker is a German speed skater known for winning an Olympic bronze medal in the 500 m event at the 2002 Winter Games.
  • B. Ute Grunert
    Ute Grunert is known as the spouse of Nobel Prize–winning German author Günter Grass.
  • C. Kathrin Zettel
    Kathrin Zettel is a retired Austrian alpine ski racer best known as a technical specialist who won multiple World Cup races and an Olympic bronze medal in slalom.
  • D. Carolin Emcke
    Carolin Emcke is a German journalist, author, and public intellectual known for her writings on violence, human rights, and social justice.
  • E. Monika Hohlmeier
    Monika Hohlmeier is a German politician from the Christian Social Union (CSU) who has served as a Member of the European Parliament, known for her work on budgetary control and civil liberties.
  • 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: Katrin Houben
Triple: [Houben, hasNotableBearer, Katrin Houben]
Generated description
Katrin Houben is an individual notable enough to be recognized as a namesake or prominent bearer of the surname Houben.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Katrin Houben
Target entity description: Katrin Houben is an individual notable enough to be recognized as a namesake or prominent bearer of the surname Houben.
  • A. Sabine Völker
    Sabine Völker is a German speed skater known for winning an Olympic bronze medal in the 500 m event at the 2002 Winter Games.
  • B. Ute Grunert
    Ute Grunert is known as the spouse of Nobel Prize–winning German author Günter Grass.
  • C. Kathrin Zettel
    Kathrin Zettel is a retired Austrian alpine ski racer best known as a technical specialist who won multiple World Cup races and an Olympic bronze medal in slalom.
  • D. Carolin Emcke
    Carolin Emcke is a German journalist, author, and public intellectual known for her writings on violence, human rights, and social justice.
  • E. Monika Hohlmeier
    Monika Hohlmeier is a German politician from the Christian Social Union (CSU) who has served as a Member of the European Parliament, known for her work on budgetary control and civil liberties.
  • 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_69ca834ca1c88190a11ffb0200342fac completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc4798852881909c03c5eadf805e49 completed March 31, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69cfa00aa93c819094884d5bbaa5a264 completed April 3, 2026, 11:10 a.m.
NEDg Description generation batch_69cfa14f0f3c8190bf7081e51410a491 completed April 3, 2026, 11:15 a.m.
NED2 Entity disambiguation (via description) batch_69cfa28589708190b8fc32f4944d0a37 completed April 3, 2026, 11:20 a.m.
Created at: March 30, 2026, 6:28 p.m.