Triple

T771718
Position Surface form Disambiguated ID Type / Status
Subject Türk Dil Kurumu E16294 entity
Predicate shortName P43 FINISHED
Object TDK
TDK is the official Turkish Language Association responsible for regulating, researching, and standardizing the Turkish language.
 E91015 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: TDK | Statement: [Türk Dil Kurumu, shortName, TDK]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TDK
Context triple: [Türk Dil Kurumu, shortName, TDK]
  • A. Toshiba
    Toshiba is a major Japanese multinational conglomerate known for its electronics, semiconductors, and information technology products and services.
  • B. Mogami
    Mogami was a lead ship of a class of Japanese World War II heavy cruisers known for their high speed, heavy armament, and participation in major Pacific naval battles.
  • C. Bose
    Bose is a common Indian surname most prominently associated with physicist Satyendra Nath Bose, whose work led to the concept of bosons and Bose–Einstein statistics.
  • D. Tokyo Tsushin Kogyo
    Tokyo Tsushin Kogyo was the original name of the Japanese electronics company that later became globally known as Sony.
  • E. Sharp Corporation
    Sharp Corporation is a Japanese multinational electronics manufacturer known for its consumer electronics, display technologies, and home appliances.
  • 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: TDK
Triple: [Türk Dil Kurumu, shortName, TDK]
Generated description
TDK is the official Turkish Language Association responsible for regulating, researching, and standardizing the Turkish language.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TDK
Target entity description: TDK is the official Turkish Language Association responsible for regulating, researching, and standardizing the Turkish language.
  • A. Toshiba
    Toshiba is a major Japanese multinational conglomerate known for its electronics, semiconductors, and information technology products and services.
  • B. Mogami
    Mogami was a lead ship of a class of Japanese World War II heavy cruisers known for their high speed, heavy armament, and participation in major Pacific naval battles.
  • C. Bose
    Bose is a common Indian surname most prominently associated with physicist Satyendra Nath Bose, whose work led to the concept of bosons and Bose–Einstein statistics.
  • D. Tokyo Tsushin Kogyo
    Tokyo Tsushin Kogyo was the original name of the Japanese electronics company that later became globally known as Sony.
  • E. Sharp Corporation
    Sharp Corporation is a Japanese multinational electronics manufacturer known for its consumer electronics, display technologies, and home appliances.
  • 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_69a49369a0848190af883934cee3db4c completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a706abf88190a1cbc2dfbbf9968a completed March 1, 2026, 8:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69a6667aabe08190b56f129864082c84 completed March 3, 2026, 4:41 a.m.
NEDg Description generation batch_69a66783f6cc819089570965e48b5749 completed March 3, 2026, 4:45 a.m.
NED2 Entity disambiguation (via description) batch_69a667e58f988190be20a9aa3c8359fd completed March 3, 2026, 4:47 a.m.
Created at: March 1, 2026, 7:37 p.m.