Triple

T1360656
Position Surface form Disambiguated ID Type / Status
Subject Southeastern Massachusetts E29090 entity
Predicate containsTown P847 FINISHED
Object Hanover
Hanover is a small suburban town in Plymouth County, Massachusetts, known for its residential character and local businesses south of Boston.
 E201806 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: [Southeastern Massachusetts, containsTown, Hanover]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hanover
Context triple: [Southeastern Massachusetts, containsTown, Hanover]
  • A. Hanover
    Hanover is a small New Hampshire town best known as the home of Dartmouth College, an Ivy League institution.
  • B. 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.
  • C. Hamburg
    Hamburg is Germany’s second-largest city and a major northern European port and cultural center on the River Elbe.
  • D. Braunschweig
    Braunschweig is a historic city in northern Germany known for its medieval architecture, cultural institutions, and role as an important economic and scientific center.
  • E. Mitte, Hanover
    Mitte, Hanover is the central district of the German city of Hanover, encompassing its historic core, main governmental buildings, and key cultural landmarks.
  • 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: [Southeastern Massachusetts, containsTown, Hanover]
Generated description
Hanover is a small suburban town in Plymouth County, Massachusetts, known for its residential character and local businesses south of Boston.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hanover
Target entity description: Hanover is a small suburban town in Plymouth County, Massachusetts, known for its residential character and local businesses south of Boston.
  • 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. Hamburg
    Hamburg is Germany’s second-largest city and a major northern European port and cultural center on the River Elbe.
  • D. Braunschweig
    Braunschweig is a historic city in northern Germany known for its medieval architecture, cultural institutions, and role as an important economic and scientific center.
  • E. Mitte, Hanover
    Mitte, Hanover is the central district of the German city of Hanover, encompassing its historic core, main governmental buildings, and key cultural landmarks.
  • 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_69a498d77abc8190913bf57e5f51d2c4 completed March 1, 2026, 7:51 p.m.
NER Named-entity recognition batch_69a4c2b156b081909c99ada70a969fc0 completed March 1, 2026, 10:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69adb5af961c8190aed3129dab0fecf3 completed March 8, 2026, 5:45 p.m.
NEDg Description generation batch_69adb8b2b01c8190997179cdfd55da13 completed March 8, 2026, 5:58 p.m.
NED2 Entity disambiguation (via description) batch_69adb94aaf348190a28ca8e9d9cacf41 completed March 8, 2026, 6 p.m.
Created at: March 1, 2026, 7:56 p.m.