Triple

T15534363
Position Surface form Disambiguated ID Type / Status
Subject Kremlings E370304 entity
Predicate notableMember P10 FINISHED
Object Krusha
Krusha is a large, muscular Kremling character from the Donkey Kong video game series, typically depicted as a strong but dim-witted enemy.
 E1162408 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: Krusha | Statement: [Kremlings, notableMember, Krusha]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Krusha
Context triple: [Kremlings, notableMember, Krusha]
  • A. Nishani
    Nishani is an Albanian surname most notably borne by Bujar Nishani, a former President of Albania.
  • B. Wonokitri
    Wonokitri is a village in East Java, Indonesia, known as a gateway settlement for visitors heading to the Mount Bromo area.
  • C. Krorayina
    Krorayina is an ancient oasis city in the Tarim Basin of present-day Xinjiang, China, known for its role as a Silk Road trading center and its well-preserved archaeological remains.
  • D. Magarima
    Magarima is a small town in Papua New Guinea’s Hela Province, serving as a local administrative and service center for surrounding rural communities.
  • E. Bhalesi
    Bhalesi is a regional dialect of the Western Pahari language spoken in parts of the western Himalayan region of India.
  • 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: Krusha
Triple: [Kremlings, notableMember, Krusha]
Generated description
Krusha is a large, muscular Kremling character from the Donkey Kong video game series, typically depicted as a strong but dim-witted enemy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Krusha
Target entity description: Krusha is a large, muscular Kremling character from the Donkey Kong video game series, typically depicted as a strong but dim-witted enemy.
  • A. Nishani
    Nishani is an Albanian surname most notably borne by Bujar Nishani, a former President of Albania.
  • B. Wonokitri
    Wonokitri is a village in East Java, Indonesia, known as a gateway settlement for visitors heading to the Mount Bromo area.
  • C. Krorayina
    Krorayina is an ancient oasis city in the Tarim Basin of present-day Xinjiang, China, known for its role as a Silk Road trading center and its well-preserved archaeological remains.
  • D. Magarima
    Magarima is a small town in Papua New Guinea’s Hela Province, serving as a local administrative and service center for surrounding rural communities.
  • E. Bhalesi
    Bhalesi is a regional dialect of the Western Pahari language spoken in parts of the western Himalayan region of India.
  • 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_69d85cc521a08190921fb50319dddc34 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e0442e327c8190b4b879c8a3cd38e3 completed April 16, 2026, 2:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff3d605b908190a18c63142c8bb854 completed May 9, 2026, 1:57 p.m.
NEDg Description generation batch_69ff3f59213c8190a9c98350225b5151 completed May 9, 2026, 2:06 p.m.
NED2 Entity disambiguation (via description) batch_69ff3ff96a6c8190a4c9f20dabc86cef completed May 9, 2026, 2:08 p.m.
Created at: April 10, 2026, 4:06 a.m.