Triple

T224973
Position Surface form Disambiguated ID Type / Status
Subject Dean Kamen E4294 entity
Predicate familyName P18 FINISHED
Object Kamen
Kamen is a surname most prominently associated with American inventor and entrepreneur Dean Kamen, known for creating the Segway and numerous medical devices.
 E34695 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: Kamen | Statement: [Dean Kamen, familyName, Kamen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kamen
Context triple: [Dean Kamen, familyName, Kamen]
  • A. Gori
    Gori is a city in central Georgia best known as the birthplace of Soviet leader Joseph Stalin.
  • B. Matsubara
    Matsubara is a suburban city in Japan’s Kansai region, located within Osaka Prefecture and forming part of the Osaka metropolitan area.
  • C. Tenjin
    Tenjin is the Shinto kami of scholarship and learning, widely revered by students seeking academic success.
  • D. Daikanyama
    Daikanyama is a trendy, upscale neighborhood in Tokyo known for its stylish boutiques, cafes, and relaxed, residential atmosphere.
  • E. Kadmat
    Kadmat is a coral island in India’s Lakshadweep archipelago, known for its white-sand beaches, clear lagoons, and vibrant marine life that make it a popular destination for snorkeling and diving.
  • 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: Kamen
Triple: [Dean Kamen, familyName, Kamen]
Generated description
Kamen is a surname most prominently associated with American inventor and entrepreneur Dean Kamen, known for creating the Segway and numerous medical devices.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kamen
Target entity description: Kamen is a surname most prominently associated with American inventor and entrepreneur Dean Kamen, known for creating the Segway and numerous medical devices.
  • A. Gori
    Gori is a city in central Georgia best known as the birthplace of Soviet leader Joseph Stalin.
  • B. Matsubara
    Matsubara is a suburban city in Japan’s Kansai region, located within Osaka Prefecture and forming part of the Osaka metropolitan area.
  • C. Tenjin
    Tenjin is the Shinto kami of scholarship and learning, widely revered by students seeking academic success.
  • D. Daikanyama
    Daikanyama is a trendy, upscale neighborhood in Tokyo known for its stylish boutiques, cafes, and relaxed, residential atmosphere.
  • E. Kadmat
    Kadmat is a coral island in India’s Lakshadweep archipelago, known for its white-sand beaches, clear lagoons, and vibrant marine life that make it a popular destination for snorkeling and diving.
  • 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_69a2573508588190b522c2476d91acfe completed Feb. 28, 2026, 2:47 a.m.
NER Named-entity recognition batch_69a25c8c0b8881908016161568c0cbfb completed Feb. 28, 2026, 3:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69a389a9bb7c81909b60569e4c6074fb completed March 1, 2026, 12:34 a.m.
NEDg Description generation batch_69a38a53a07881908a1e1a0773680044 completed March 1, 2026, 12:37 a.m.
NED2 Entity disambiguation (via description) batch_69a38acac1ec81909578321bb8b0bfd2 completed March 1, 2026, 12:39 a.m.
Created at: Feb. 28, 2026, 2:53 a.m.