Triple

T8812214
Position Surface form Disambiguated ID Type / Status
Subject Belgrade Lakes region E209691 entity
Predicate hasPart P35 FINISHED
Object Snow Pond
Snow Pond is a scenic lake in Maine’s Belgrade Lakes region, known for its recreational opportunities and natural beauty.
 E779237 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: Snow Pond | Statement: [Belgrade Lakes region, hasPart, Snow Pond]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Snow Pond
Context triple: [Belgrade Lakes region, hasPart, Snow Pond]
  • A. Tivoli Pond
    Tivoli Pond is a scenic ornamental lake within Ljubljana’s historic Tivoli Park, known for its tranquil setting, wildlife, and recreational appeal.
  • B. Long Pond
    Long Pond is a large natural freshwater lake in Lakeville, Massachusetts, known for recreational boating, fishing, and its scenic residential shoreline.
  • C. Long Pond
    Long Pond is a body of water located within the town of Freetown, Massachusetts, known for recreational activities such as boating and fishing.
  • D. Long Pond
    Long Pond is a scenic freshwater lake in Maine’s Belgrade Lakes region, popular for boating, fishing, and lakeside recreation.
  • E. Daicey Pond
    Daicey Pond is a scenic, tranquil pond in Maine known for its views of Mount Katahdin and its popular canoeing, fishing, and camping opportunities.
  • 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: Snow Pond
Triple: [Belgrade Lakes region, hasPart, Snow Pond]
Generated description
Snow Pond is a scenic lake in Maine’s Belgrade Lakes region, known for its recreational opportunities and natural beauty.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Snow Pond
Target entity description: Snow Pond is a scenic lake in Maine’s Belgrade Lakes region, known for its recreational opportunities and natural beauty.
  • A. Tivoli Pond
    Tivoli Pond is a scenic ornamental lake within Ljubljana’s historic Tivoli Park, known for its tranquil setting, wildlife, and recreational appeal.
  • B. Long Pond
    Long Pond is a large natural freshwater lake in Lakeville, Massachusetts, known for recreational boating, fishing, and its scenic residential shoreline.
  • C. Long Pond
    Long Pond is a scenic freshwater lake in Maine’s Belgrade Lakes region, popular for boating, fishing, and lakeside recreation.
  • D. Long Pond
    Long Pond is a body of water located within the town of Freetown, Massachusetts, known for recreational activities such as boating and fishing.
  • E. Daicey Pond
    Daicey Pond is a scenic, tranquil pond in Maine known for its views of Mount Katahdin and its popular canoeing, fishing, and camping opportunities.
  • 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_69ca8363f3308190a47e3f1ebd51f613 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5feed07881909bbe116ae359346a completed March 31, 2026, 11:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69d02f6e221081909a8a83f2e465b1c2 completed April 3, 2026, 9:21 p.m.
NEDg Description generation batch_69d0343054388190949c9b4ec2492aaf completed April 3, 2026, 9:42 p.m.
NED2 Entity disambiguation (via description) batch_69d0347b762c8190913dc347cde6cbb0 completed April 3, 2026, 9:43 p.m.
Created at: March 30, 2026, 6:45 p.m.