Triple

T3124051
Position Surface form Disambiguated ID Type / Status
Subject Zaria E65252 entity
Predicate hasPart P35 FINISHED
Object Tudun Wada
Tudun Wada is a district within the city of Zaria in Kaduna State, northern Nigeria.
E328707 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: Tudun Wada | Statement: [Zaria, hasPart, Tudun Wada]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tudun Wada
Context triple: [Zaria, hasPart, Tudun Wada]
  • A. Tamada
    Tamada is the traditional Georgian toastmaster who leads feasts and orchestrates toasts during the supra, Georgia’s ceremonial banquet.
  • B. Takanami
    Takanami was a Japanese destroyer of the Imperial Japanese Navy during World War II, notable for being sunk in the Battle of Tassafaronga in 1942.
  • C. Yodo-dono
    Yodo-dono was a prominent Japanese noblewoman and political figure of the late Sengoku period, best known as Toyotomi Hideyoshi’s consort and the mother of his heir, Toyotomi Hideyori.
  • D. Nezu
    Nezu is a traditional neighborhood in Tokyo known for its historic Nezu Shrine, old-town atmosphere, and preserved shitamachi streets.
  • E. Tatsuno Kingo
    Tatsuno Kingo was a prominent Japanese architect of the Meiji era, best known for pioneering Western-style brick architecture in Japan and designing landmark buildings such as Tokyo Station.
  • 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: Tudun Wada
Triple: [Zaria, hasPart, Tudun Wada]
Generated description
Tudun Wada is a district within the city of Zaria in Kaduna State, northern Nigeria.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Tudun Wada
Target entity description: Tudun Wada is a district within the city of Zaria in Kaduna State, northern Nigeria.
  • A. Tamada
    Tamada is the traditional Georgian toastmaster who leads feasts and orchestrates toasts during the supra, Georgia’s ceremonial banquet.
  • B. Takanami
    Takanami was a Japanese destroyer of the Imperial Japanese Navy during World War II, notable for being sunk in the Battle of Tassafaronga in 1942.
  • C. Yodo-dono
    Yodo-dono was a prominent Japanese noblewoman and political figure of the late Sengoku period, best known as Toyotomi Hideyoshi’s consort and the mother of his heir, Toyotomi Hideyori.
  • D. Nezu
    Nezu is a traditional neighborhood in Tokyo known for its historic Nezu Shrine, old-town atmosphere, and preserved shitamachi streets.
  • E. Tatsuno Kingo
    Tatsuno Kingo was a prominent Japanese architect of the Meiji era, best known for pioneering Western-style brick architecture in Japan and designing landmark buildings such as Tokyo Station.
  • 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_69ad8580c72481909672d37acf647893 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada52d856c8190a5d65b8a6452be21 completed March 8, 2026, 4:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69b20f72c2048190ab2aa40a109f5976 completed March 12, 2026, 12:57 a.m.
NEDg Description generation batch_69b21083db7081908f8bc4240fc2b08b completed March 12, 2026, 1:01 a.m.
NED2 Entity disambiguation (via description) batch_69b210fa8a7c8190ae4527161aa3af54 completed March 12, 2026, 1:03 a.m.
Created at: March 8, 2026, 3:04 p.m.