Triple

T9213352
Position Surface form Disambiguated ID Type / Status
Subject Mads Nipper E221179 entity
Predicate givenName P17 FINISHED
Object Mads
Mads is a Scandinavian given name commonly used for males, particularly in Denmark and Norway.
 E785470 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: Mads | Statement: [Mads Nipper, givenName, Mads]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mads
Context triple: [Mads Nipper, givenName, Mads]
  • A. Mads Dittmann Mikkelsen
    Mads Dittmann Mikkelsen is a Danish actor renowned for his versatile performances in films and television series such as "Casino Royale," "Hannibal," and "Another Round."
  • B. Jørgen
    Jørgen is a Scandinavian male given name, commonly used in Denmark and Norway and related to the name George.
  • C. Søren
    Søren is a masculine given name of Scandinavian origin, most famously borne by the Danish philosopher Søren Kierkegaard.
  • D. Morten
    Morten is a masculine given name commonly used in Scandinavian countries, derived from the Latin name Martinus.
  • E. Rasmus
    Rasmus is a masculine given name of Scandinavian origin, commonly used in countries such as Denmark, Norway, and Sweden.
  • 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: Mads
Triple: [Mads Nipper, givenName, Mads]
Generated description
Mads is a Scandinavian given name commonly used for males, particularly in Denmark and Norway.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mads
Target entity description: Mads is a Scandinavian given name commonly used for males, particularly in Denmark and Norway.
  • A. Mads Dittmann Mikkelsen
    Mads Dittmann Mikkelsen is a Danish actor renowned for his versatile performances in films and television series such as "Casino Royale," "Hannibal," and "Another Round."
  • B. Jørgen
    Jørgen is a Scandinavian male given name, commonly used in Denmark and Norway and related to the name George.
  • C. Søren
    Søren is a masculine given name of Scandinavian origin, most famously borne by the Danish philosopher Søren Kierkegaard.
  • D. Morten
    Morten is a masculine given name commonly used in Scandinavian countries, derived from the Latin name Martinus.
  • E. Rasmus
    Rasmus is a masculine given name of Scandinavian origin, commonly used in countries such as Denmark, Norway, and Sweden.
  • 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_69ca83eae42c8190a0ea9e040710a277 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccda05406081909893bec3a092d3ce completed April 1, 2026, 8:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69d06613daf88190a0128fd53ea1b134 completed April 4, 2026, 1:15 a.m.
NEDg Description generation batch_69d0678b89ac8190b807e1c3b457a503 completed April 4, 2026, 1:21 a.m.
NED2 Entity disambiguation (via description) batch_69d0688d4c388190bb024b03cc86d08f completed April 4, 2026, 1:25 a.m.
Created at: March 30, 2026, 7:27 p.m.