Triple

T2966392
Position Surface form Disambiguated ID Type / Status
Subject Kitami E80174 entity
Predicate hasSubdivision P747 FINISHED
Object Tanno
Tanno is a district or locality within the city of Kitami in Hokkaido, Japan.
 E324333 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: Tanno | Statement: [Kitami, hasSubdivision, Tanno]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tanno
Context triple: [Kitami, hasSubdivision, Tanno]
  • A. Machimura
    Machimura is a Japanese surname most notably associated with Nobutaka Machimura, a prominent Liberal Democratic Party politician and former foreign minister of Japan.
  • B. Yasu
    Yasu is a Japanese city located in Shiga Prefecture, known for its blend of residential areas, local industry, and proximity to Lake Biwa.
  • C. Rairi
    Rairi is the historical name of Raigad Fort, a prominent hill fort in Maharashtra, India, closely associated with the Maratha ruler Chhatrapati Shivaji Maharaj.
  • D. Wako
    Wako is a suburban city in Saitama Prefecture, Japan, located on the northern outskirts of Tokyo and known as a residential and commuter hub.
  • E. Moruya
    Moruya is a coastal town in New South Wales, Australia, known for its scenic river setting, nearby beaches, and historic granite quarries.
  • 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: Tanno
Triple: [Kitami, hasSubdivision, Tanno]
Generated description
Tanno is a district or locality within the city of Kitami in Hokkaido, Japan.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tanno
Target entity description: Tanno is a district or locality within the city of Kitami in Hokkaido, Japan.
  • A. Machimura
    Machimura is a Japanese surname most notably associated with Nobutaka Machimura, a prominent Liberal Democratic Party politician and former foreign minister of Japan.
  • B. Yasu
    Yasu is a Japanese city located in Shiga Prefecture, known for its blend of residential areas, local industry, and proximity to Lake Biwa.
  • C. Rairi
    Rairi is the historical name of Raigad Fort, a prominent hill fort in Maharashtra, India, closely associated with the Maratha ruler Chhatrapati Shivaji Maharaj.
  • D. Wako
    Wako is a suburban city in Saitama Prefecture, Japan, located on the northern outskirts of Tokyo and known as a residential and commuter hub.
  • E. Moruya
    Moruya is a coastal town in New South Wales, Australia, known for its scenic river setting, nearby beaches, and historic granite quarries.
  • 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_69ad8b1341848190bd19dbf46892887d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad996e93788190ba9883714d4dfa0c completed March 8, 2026, 3:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69b1f85f5fa481909785b6c59b4197fb completed March 11, 2026, 11:18 p.m.
NEDg Description generation batch_69b1f8e5cdd08190840321d7ad1fe1e2 completed March 11, 2026, 11:21 p.m.
NED2 Entity disambiguation (via description) batch_69b1f968f17c81908b1e96f482546b80 completed March 11, 2026, 11:23 p.m.
Created at: March 8, 2026, 2:58 p.m.