Triple

T556982
Position Surface form Disambiguated ID Type / Status
Subject Division of Riverina E11962 entity
Predicate includesTown P847 FINISHED
Object Yenda
Yenda is a small rural town in the Riverina region of New South Wales, Australia, known for its agricultural production, particularly viticulture and horticulture.
 E79595 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: Yenda | Statement: [Division of Riverina, includesTown, Yenda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Yenda
Context triple: [Division of Riverina, includesTown, Yenda]
  • A. Negombo
    Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
  • B. Yamba
    Yamba is a coastal town in northern New South Wales, Australia, known for its beaches, fishing, and laid-back holiday atmosphere.
  • C. Kasulu
    Kasulu is a town in western Tanzania that serves as one of the main urban and commercial centers of the Kigoma Region.
  • D. Wanetsi
    Wanetsi is a distinct and archaic variety of Pashto spoken by a small community in parts of Afghanistan and Pakistan.
  • E. Fiambalá
    Fiambalá is a small town in northwestern Argentina known for its high-altitude vineyards, desert landscapes, and nearby Andean mountain passes.
  • 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: Yenda
Triple: [Division of Riverina, includesTown, Yenda]
Generated description
Yenda is a small rural town in the Riverina region of New South Wales, Australia, known for its agricultural production, particularly viticulture and horticulture.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Yenda
Target entity description: Yenda is a small rural town in the Riverina region of New South Wales, Australia, known for its agricultural production, particularly viticulture and horticulture.
  • A. Negombo
    Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
  • B. Yamba
    Yamba is a coastal town in northern New South Wales, Australia, known for its beaches, fishing, and laid-back holiday atmosphere.
  • C. Kasulu
    Kasulu is a town in western Tanzania that serves as one of the main urban and commercial centers of the Kigoma Region.
  • D. Wanetsi
    Wanetsi is a distinct and archaic variety of Pashto spoken by a small community in parts of Afghanistan and Pakistan.
  • E. Fiambalá
    Fiambalá is a small town in northwestern Argentina known for its high-altitude vineyards, desert landscapes, and nearby Andean mountain passes.
  • 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_69a4932941d08190815efd422f0b4ca7 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49d28af148190acad3cfb809ff2f2 completed March 1, 2026, 8:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69a56ee8472081908f3d3bed26a40aca completed March 2, 2026, 11:05 a.m.
NEDg Description generation batch_69a5714659dc8190aac2b41e4e149997 completed March 2, 2026, 11:15 a.m.
NED2 Entity disambiguation (via description) batch_69a571a98c208190872831a707419dc3 completed March 2, 2026, 11:16 a.m.
Created at: March 1, 2026, 7:32 p.m.