Triple

T262078
Position Surface form Disambiguated ID Type / Status
Subject Tokyo E5560 entity
Predicate hasVehicleRegistrationCode P1173 FINISHED
Object Tama
Tama is a region in western Tokyo, Japan, encompassing several suburban cities and towns that serve as residential and commercial areas for the greater Tokyo metropolis.
 E38268 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: Tama | Statement: [Tokyo, hasVehicleRegistrationCode, Tama]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tama
Context triple: [Tokyo, hasVehicleRegistrationCode, Tama]
  • A. Kato
    Kato is the nickname of Kato Svanidze, who was the first wife of Soviet leader Joseph Stalin.
  • B. Taro
    Taro is a common Japanese male given name, often written with kanji meaning "eldest son" or similar traditional connotations.
  • C. Tamada
    Tamada is the traditional Georgian toastmaster who leads feasts and orchestrates toasts during the supra, Georgia’s ceremonial banquet.
  • D. Jimintō
    Jimintō is the dominant conservative political party in Japan, formally known as the Liberal Democratic Party.
  • E. Ebisu
    Ebisu is a fashionable Tokyo neighborhood known for its upscale dining, craft beer scene, and convenient access via Ebisu Station near Shibuya.
  • 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: Tama
Triple: [Tokyo, hasVehicleRegistrationCode, Tama]
Generated description
Tama is a region in western Tokyo, Japan, encompassing several suburban cities and towns that serve as residential and commercial areas for the greater Tokyo metropolis.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tama
Target entity description: Tama is a region in western Tokyo, Japan, encompassing several suburban cities and towns that serve as residential and commercial areas for the greater Tokyo metropolis.
  • A. Kato
    Kato is the nickname of Kato Svanidze, who was the first wife of Soviet leader Joseph Stalin.
  • B. Taro
    Taro is a common Japanese male given name, often written with kanji meaning "eldest son" or similar traditional connotations.
  • C. Tamada
    Tamada is the traditional Georgian toastmaster who leads feasts and orchestrates toasts during the supra, Georgia’s ceremonial banquet.
  • D. Jimintō
    Jimintō is the dominant conservative political party in Japan, formally known as the Liberal Democratic Party.
  • E. Ebisu
    Ebisu is a fashionable Tokyo neighborhood known for its upscale dining, craft beer scene, and convenient access via Ebisu Station near Shibuya.
  • 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_69a2580a64ac8190ad76e34bb0715b5e completed Feb. 28, 2026, 2:50 a.m.
NER Named-entity recognition batch_69a25d7428dc8190ae12b12a21fcc6cb completed Feb. 28, 2026, 3:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69a3a88442708190af1193469316f757 completed March 1, 2026, 2:46 a.m.
NEDg Description generation batch_69a3a90299888190b7f88d6411531823 completed March 1, 2026, 2:48 a.m.
NED2 Entity disambiguation (via description) batch_69a3a96d153081909fab6bace45206ec completed March 1, 2026, 2:50 a.m.
Created at: Feb. 28, 2026, 2:55 a.m.