Triple

T1135507
Position Surface form Disambiguated ID Type / Status
Subject Hyogo Prefecture E23129 entity
Predicate hasCity P316 FINISHED
Object Tamba
Tamba is a city located in Hyogo Prefecture, Japan, known for its rural landscapes, traditional pottery, and historical sites.
 E138991 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: Tamba | Statement: [Hyogo Prefecture, hasCity, Tamba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Tamba
Context triple: [Hyogo Prefecture, hasCity, Tamba]
  • A. Nembe
    Nembe is an Ijaw subgroup and town in Bayelsa State, Nigeria, known historically as a coastal trading center in the Niger Delta.
  • B. Wainganga
    Wainganga is a major river in central India that flows through the states of Madhya Pradesh and Maharashtra before joining other rivers on its way to the Godavari basin.
  • C. Mvita
    Mvita is an alternative name for Kimvita, a historic Swahili settlement and cultural center on the coast of present-day Kenya.
  • D. Lusiana
    Lusiana is a small town in the Veneto region of northern Italy, known as the birthplace of Indian politician Sonia Gandhi.
  • E. Kasulu
    Kasulu is a town in western Tanzania that serves as one of the main urban and commercial centers of the Kigoma Region.
  • 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: Tamba
Triple: [Hyogo Prefecture, hasCity, Tamba]
Generated description
Tamba is a city located in Hyogo Prefecture, Japan, known for its rural landscapes, traditional pottery, and historical sites.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Tamba
Target entity description: Tamba is a city located in Hyogo Prefecture, Japan, known for its rural landscapes, traditional pottery, and historical sites.
  • A. Nembe
    Nembe is an Ijaw subgroup and town in Bayelsa State, Nigeria, known historically as a coastal trading center in the Niger Delta.
  • B. Wainganga
    Wainganga is a major river in central India that flows through the states of Madhya Pradesh and Maharashtra before joining other rivers on its way to the Godavari basin.
  • C. Mvita
    Mvita is an alternative name for Kimvita, a historic Swahili settlement and cultural center on the coast of present-day Kenya.
  • D. Lusiana
    Lusiana is a small town in the Veneto region of northern Italy, known as the birthplace of Indian politician Sonia Gandhi.
  • E. Kasulu
    Kasulu is a town in western Tanzania that serves as one of the main urban and commercial centers of the Kigoma Region.
  • 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_69a493ec75988190b63a11bafaec29b4 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4bc2300c481908c60fbb1188c37c5 completed March 1, 2026, 10:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac830d57e0819086fd19e032a589cd completed March 7, 2026, 7:57 p.m.
NEDg Description generation batch_69ac837e06cc8190b0da34646fa78c0c completed March 7, 2026, 7:58 p.m.
NED2 Entity disambiguation (via description) batch_69ac84309acc8190aac6c3c78246b352 completed March 7, 2026, 8:01 p.m.
Created at: March 1, 2026, 7:44 p.m.