Triple

T9765480
Position Surface form Disambiguated ID Type / Status
Subject Mobile Fleet E236977 entity
Predicate notableShip P3345 FINISHED
Object Chikuma
Chikuma was a Japanese Imperial Navy heavy cruiser that served prominently in World War II, including major Pacific naval battles.
 E881729 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: Chikuma | Statement: [Mobile Fleet, notableShip, Chikuma]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Chikuma
Context triple: [Mobile Fleet, notableShip, Chikuma]
  • A. Chikugo
    Chikugo is a city in southwestern Japan known for its agricultural production and traditional crafts within Fukuoka Prefecture on Kyushu Island.
  • B. Takinogawa
    Takinogawa is a residential district in Kita Ward, Tokyo, known for its quiet neighborhoods and convenient urban access.
  • C. Kizugawa
    Kizugawa is a city in southern Kyoto Prefecture, Japan, known for its mix of historical sites, residential areas, and growing industrial and research facilities.
  • D. Kisogawa
    Kisogawa is the Japanese name for the Kiso River, a major river in central Honshu known for its scenic valleys and historical importance.
  • E. Kamogawa
    Kamogawa is a coastal city in Chiba Prefecture, Japan, known for its beaches, fishing industry, and the popular Kamogawa Sea World aquarium.
  • 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: Chikuma
Triple: [Mobile Fleet, notableShip, Chikuma]
Generated description
Chikuma was a Japanese Imperial Navy heavy cruiser that served prominently in World War II, including major Pacific naval battles.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Chikuma
Target entity description: Chikuma was a Japanese Imperial Navy heavy cruiser that served prominently in World War II, including major Pacific naval battles.
  • A. Chikugo
    Chikugo is a city in southwestern Japan known for its agricultural production and traditional crafts within Fukuoka Prefecture on Kyushu Island.
  • B. Takinogawa
    Takinogawa is a residential district in Kita Ward, Tokyo, known for its quiet neighborhoods and convenient urban access.
  • C. Kizugawa
    Kizugawa is a city in southern Kyoto Prefecture, Japan, known for its mix of historical sites, residential areas, and growing industrial and research facilities.
  • D. Kisogawa
    Kisogawa is the Japanese name for the Kiso River, a major river in central Honshu known for its scenic valleys and historical importance.
  • E. Kamogawa
    Kamogawa is a coastal city in Chiba Prefecture, Japan, known for its beaches, fishing industry, and the popular Kamogawa Sea World aquarium.
  • 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_69ca84d831b8819090322686b47887ce completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cda0a040988190b1c940f9e5c42f9c completed April 1, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69dbb6bb34548190aac9d2af05477750 completed April 12, 2026, 3:14 p.m.
NEDg Description generation batch_69dbbb8b0e5c8190afa9aaa134bcebf2 completed April 12, 2026, 3:34 p.m.
NED2 Entity disambiguation (via description) batch_69dbbc3ce1008190a16d442a22d45967 completed April 12, 2026, 3:37 p.m.
Created at: March 30, 2026, 8:25 p.m.