Triple

T20960762
Position Surface form Disambiguated ID Type / Status
Subject Grey County E516236 entity
Predicate containsSettlement P847 FINISHED
Object Hanover
Hanover is a small town in southwestern Ontario, Canada, known for its manufacturing base and regional services within Grey County.
 E1459713 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: Hanover | Statement: [Grey County, containsSettlement, Hanover]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hanover
Context triple: [Grey County, containsSettlement, Hanover]
  • A. Hanover
    Hanover is a historic city in northern Germany that served as the capital of the former Kingdom of Hanover and the ancestral seat of the British House of Hanover.
  • B. Hanover
    Hanover is a small suburban town in Plymouth County, Massachusetts, known for its residential character and local businesses south of Boston.
  • C. Hanover
    Hanover is a small town in South Africa, known as the birthplace of prominent trade unionist Zwelinzima Vavi.
  • D. Hanover
    Hanover is a small New Hampshire town best known as the home of Dartmouth College, an Ivy League institution.
  • E. Hanover
    Hanover is a surname most notably associated with Donna Hanover, an American journalist, actress, and former First Lady of New York City.
  • 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: Hanover
Triple: [Grey County, containsSettlement, Hanover]
Generated description
Hanover is a small town in southwestern Ontario, Canada, known for its manufacturing base and regional services within Grey County.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hanover
Target entity description: Hanover is a small town in southwestern Ontario, Canada, known for its manufacturing base and regional services within Grey County.
  • A. Hanover
    Hanover is a historic city in northern Germany that served as the capital of the former Kingdom of Hanover and the ancestral seat of the British House of Hanover.
  • B. Hanover
    Hanover is a small New Hampshire town best known as the home of Dartmouth College, an Ivy League institution.
  • C. Hanover
    Hanover is a small suburban town in Plymouth County, Massachusetts, known for its residential character and local businesses south of Boston.
  • D. Hanover
    Hanover is a surname most notably associated with Donna Hanover, an American journalist, actress, and former First Lady of New York City.
  • E. Hanover
    Hanover is a small town in South Africa, known as the birthplace of prominent trade unionist Zwelinzima Vavi.
  • 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_69e0b4fde6c48190af1398e7e734629e completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6fb6f134081908b1ed48ce708f3d5 completed April 21, 2026, 4:22 a.m.
NED1 Entity disambiguation (via context triple) batch_6a092798800481909d437b58467f8e2b completed May 17, 2026, 2:27 a.m.
NEDg Description generation batch_6a0928b4be448190bd862c8f971e61d9 completed May 17, 2026, 2:32 a.m.
NED2 Entity disambiguation (via description) batch_6a0929734a5c8190911e54b601118bae completed May 17, 2026, 2:35 a.m.
Created at: April 16, 2026, 1:31 p.m.