Triple

T15489274
Position Surface form Disambiguated ID Type / Status
Subject Hankyu 3300 series E377135 entity
Predicate manufacturer P490 FINISHED
Object Alna Kōki
Alna Kōki is a Japanese rolling stock manufacturer known for producing electric multiple units and other railway vehicles for private railway operators.
 E1169960 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: Alna Kōki | Statement: [Hankyu 3300 series, manufacturer, Alna Kōki]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Alna Kōki
Context triple: [Hankyu 3300 series, manufacturer, Alna Kōki]
  • A. Tsutako
    Tsutako is a Japanese given name, most notably borne by Tsutako Nakasone.
  • B. Kōki
    Kōki is a Japanese given name commonly used for males and borne by various notable figures in Japan.
  • C. Koyuki
    Koyuki is a Japanese actress and model best known internationally for her role opposite Tom Cruise in the film "The Last Samurai."
  • D. Yamanakako
    Yamanakako is a village in Yamanashi Prefecture, Japan, known for Lake Yamanaka, one of the Fuji Five Lakes located near Mount Fuji.
  • E. Sanae
    Sanae is a Japanese feminine given name borne by various notable figures in politics, entertainment, and other fields.
  • 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: Alna Kōki
Triple: [Hankyu 3300 series, manufacturer, Alna Kōki]
Generated description
Alna Kōki is a Japanese rolling stock manufacturer known for producing electric multiple units and other railway vehicles for private railway operators.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Alna Kōki
Target entity description: Alna Kōki is a Japanese rolling stock manufacturer known for producing electric multiple units and other railway vehicles for private railway operators.
  • A. Tsutako
    Tsutako is a Japanese given name, most notably borne by Tsutako Nakasone.
  • B. Kōki
    Kōki is a Japanese given name commonly used for males and borne by various notable figures in Japan.
  • C. Koyuki
    Koyuki is a Japanese actress and model best known internationally for her role opposite Tom Cruise in the film "The Last Samurai."
  • D. Yamanakako
    Yamanakako is a village in Yamanashi Prefecture, Japan, known for Lake Yamanaka, one of the Fuji Five Lakes located near Mount Fuji.
  • E. Sanae
    Sanae is a Japanese feminine given name borne by various notable figures in politics, entertainment, and other fields.
  • 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_69d85cd21dcc81908646251b1c26ea00 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03faaca588190b0397bc2e27a522a completed April 16, 2026, 1:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff6780ee3081908a0a833d887b1829 completed May 9, 2026, 4:57 p.m.
NEDg Description generation batch_69ff68947cec8190a77cfe560a10a1ee completed May 9, 2026, 5:02 p.m.
NED2 Entity disambiguation (via description) batch_69ff69370ed081908ab61470f126bcf9 completed May 9, 2026, 5:04 p.m.
Created at: April 10, 2026, 3:48 a.m.