Triple

T19463870
Position Surface form Disambiguated ID Type / Status
Subject Lake Minnetonka E486942 entity
Predicate hasCityOnShore P969 FINISHED
Object Woodland
Woodland is a small, affluent residential city in Minnesota known for its wooded setting and location along the shores of Lake Minnetonka.
 E353236 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: Woodland | Statement: [Lake Minnetonka, hasCityOnShore, Woodland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Woodland
Context triple: [Lake Minnetonka, hasCityOnShore, Woodland]
  • A. Woodland
    Woodland is a light rail station on Boston’s MBTA Green Line D branch serving the Newton area.
  • B. Woodland
    Woodland is a small city located in Talbot County, Georgia, in the United States.
  • C. Woodland
    Woodland is a small rural community located in the state of Maine in the United States.
  • D. Woodland
    Woodland is a small city in California’s Sacramento Valley known as an agricultural and administrative hub for Yolo County.
  • E. Woodland
    Woodland is an episode of the British nature documentary series "Wild Isles" that explores the wildlife and ecosystems of the United Kingdom’s forests and woodlands.
  • 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: Woodland
Triple: [Lake Minnetonka, hasCityOnShore, Woodland]
Generated description
Woodland is a small, affluent residential city in Minnesota known for its wooded setting and location along the shores of Lake Minnetonka.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Woodland
Target entity description: Woodland is a small, affluent residential city in Minnesota known for its wooded setting and location along the shores of Lake Minnetonka.
  • A. Woodland chosen
    Woodland is a small, affluent residential city located in Hennepin County, Minnesota, known for its wooded landscapes and lakeside properties.
  • B. Woodland
    Woodland is a small city in California’s Sacramento Valley known as an agricultural and administrative hub for Yolo County.
  • C. Woodland
    Woodland is a small rural community located in the state of Maine in the United States.
  • D. Woodland
    Woodland is a small city located in Talbot County, Georgia, in the United States.
  • E. Woodland
    Woodland is a light rail station on Boston’s MBTA Green Line D branch serving the Newton area.
  • F. None of above.

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_69d8e8d86d608190bd199a98d0297f27 completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e633cf95988190b13b2153e67d0cac completed April 20, 2026, 2:10 p.m.
NED1 Entity disambiguation (via context triple) batch_6a073b3e3e88819090a307eeabc2a180 completed May 15, 2026, 3:26 p.m.
NEDg Description generation batch_6a073c0a02c8819080b0e71caf65e6fa completed May 15, 2026, 3:30 p.m.
NED2 Entity disambiguation (via description) batch_6a073d0e92d48190880317263fe6e481 completed May 15, 2026, 3:34 p.m.
Created at: April 10, 2026, 1:38 p.m.