Triple

T2424920
Position Surface form Disambiguated ID Type / Status
Subject Expo 85 E53503 entity
Predicate city P40 FINISHED
Object Tsukuba
Tsukuba is a planned science and technology city in Ibaraki Prefecture, Japan, known for its research institutions and role as the host of the 1985 World Exposition.
 E526806 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: Tsukuba | Statement: [Expo 85, city, Tsukuba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tsukuba
Context triple: [Expo 85, city, Tsukuba]
  • A. Takasaki
    Takasaki is a city in Japan’s Gunma Prefecture known for its Daruma doll production and as a regional commercial and transportation hub.
  • B. Akishima
    Akishima is a city in western Tokyo, Japan, known as part of the Tama area and characterized by its residential neighborhoods and light industry.
  • C. Maebashi
    Maebashi is the capital city of Gunma Prefecture in Japan, known as a regional administrative and commercial center on the Kantō Plain.
  • D. Ibaraki City
    Ibaraki City is a suburban city in northern Osaka Prefecture, Japan, known as a residential and commercial hub between Osaka and Kyoto.
  • E. Yokkaichi
    Yokkaichi is an industrial port city in central Japan known for its petrochemical complexes and role as a major manufacturing hub.
  • 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: Tsukuba
Triple: [Expo 85, city, Tsukuba]
Generated description
Tsukuba is a planned science and technology city in Ibaraki Prefecture, Japan, known for its research institutions and role as the host of the 1985 World Exposition.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tsukuba
Target entity description: Tsukuba is a planned science and technology city in Ibaraki Prefecture, Japan, known for its research institutions and role as the host of the 1985 World Exposition.
  • A. Takasaki
    Takasaki is a city in Japan’s Gunma Prefecture known for its Daruma doll production and as a regional commercial and transportation hub.
  • B. Akishima
    Akishima is a city in western Tokyo, Japan, known as part of the Tama area and characterized by its residential neighborhoods and light industry.
  • C. Maebashi
    Maebashi is the capital city of Gunma Prefecture in Japan, known as a regional administrative and commercial center on the Kantō Plain.
  • D. Ibaraki City
    Ibaraki City is a suburban city in northern Osaka Prefecture, Japan, known as a residential and commercial hub between Osaka and Kyoto.
  • E. Yokkaichi
    Yokkaichi is an industrial port city in central Japan known for its petrochemical complexes and role as a major manufacturing hub.
  • 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_69ab495c44d48190b7235b23719bc3f6 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abc99a773c819092d5f3c297b83887 completed March 7, 2026, 6:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69bfc9d292808190bb0213c52393b153 completed March 22, 2026, 10:52 a.m.
NEDg Description generation batch_69bfca7d89e08190a0505b7b786adba6 completed March 22, 2026, 10:54 a.m.
NED2 Entity disambiguation (via description) batch_69bfcad35758819093b5928b08f19899 completed March 22, 2026, 10:56 a.m.
Created at: March 6, 2026, 9:42 p.m.