Triple

T21716738
Position Surface form Disambiguated ID Type / Status
Subject Kagerō-class destroyer E536049 entity
Predicate hasPart P35 FINISHED
Object Tanikaze
Tanikaze was an Imperial Japanese Navy destroyer of World War II, noted for its participation in several major Pacific naval battles.
 E1500349 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: Tanikaze | Statement: [Kagerō-class destroyer, hasPart, Tanikaze]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tanikaze
Context triple: [Kagerō-class destroyer, hasPart, Tanikaze]
  • A. Tateishi
    Tateishi is a neighborhood in Tokyo known for its traditional shitamachi atmosphere, narrow shopping streets, and old-style bars and eateries.
  • B. Takanami
    Takanami was a Japanese destroyer of the Imperial Japanese Navy during World War II, notable for being sunk in the Battle of Tassafaronga in 1942.
  • C. Takeaki
    Takeaki is a Japanese given name most notably borne by Enomoto Takeaki, a 19th-century samurai, admiral, and statesman.
  • D. Toshiki
    Toshiki is a Japanese masculine given name borne by various notable individuals in politics, entertainment, and sports.
  • E. Kazuno
    Kazuno is a city in northern Japan known for its hot springs, traditional festivals, and mountainous rural scenery.
  • 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: Tanikaze
Triple: [Kagerō-class destroyer, hasPart, Tanikaze]
Generated description
Tanikaze was an Imperial Japanese Navy destroyer of World War II, noted for its participation in several major Pacific naval battles.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tanikaze
Target entity description: Tanikaze was an Imperial Japanese Navy destroyer of World War II, noted for its participation in several major Pacific naval battles.
  • A. Tateishi
    Tateishi is a neighborhood in Tokyo known for its traditional shitamachi atmosphere, narrow shopping streets, and old-style bars and eateries.
  • B. Takanami
    Takanami was a Japanese destroyer of the Imperial Japanese Navy during World War II, notable for being sunk in the Battle of Tassafaronga in 1942.
  • C. Takeaki
    Takeaki is a Japanese given name most notably borne by Enomoto Takeaki, a 19th-century samurai, admiral, and statesman.
  • D. Toshiki
    Toshiki is a Japanese masculine given name borne by various notable individuals in politics, entertainment, and sports.
  • E. Kazuno
    Kazuno is a city in northern Japan known for its hot springs, traditional festivals, and mountainous rural scenery.
  • 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_69e0c46c6dd88190a595375fa6ebd701 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69efd96ab4c88190b76f4a6b7c855039 completed April 27, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a366672f48190a9dc56e2918e4a7f completed May 17, 2026, 9:43 p.m.
NEDg Description generation batch_6a0a3779ea6c819099bcb8e8f9230dc9 completed May 17, 2026, 9:47 p.m.
NED2 Entity disambiguation (via description) batch_6a0a3805d96c81908f03dbdafe28e805 completed May 17, 2026, 9:49 p.m.
Created at: April 16, 2026, 6:47 p.m.