Triple

T38467660
Position Surface form Disambiguated ID Type / Status
Subject Mauston, Wisconsin E912615 entity
Predicate hasNearbyAttraction P2064 FINISHED
Object Buckhorn State Park
Buckhorn State Park is a Wisconsin state park known for its extensive shoreline, camping, and outdoor recreation opportunities on Castle Rock Lake.
E2282893 NE FINISHED

How this triple was built (2 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: Buckhorn State Park | Statement: [Mauston, Wisconsin, hasNearbyAttraction, Buckhorn State Park]
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: Buckhorn State Park
Triple: [Mauston, Wisconsin, hasNearbyAttraction, Buckhorn State Park]
Generated description
Buckhorn State Park is a Wisconsin state park known for its extensive shoreline, camping, and outdoor recreation opportunities on Castle Rock Lake.

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_69f76e861d8c81908559031dc66e3c15 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fcd1fbc0fc8190a0ef4f1ebb215d0d completed May 7, 2026, 5:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a423419d0e881909f3d5a1990bb8a43 completed June 29, 2026, 9 a.m.
NEDg Description generation batch_6a4234e9228c8190a9de309d092e6646 completed June 29, 2026, 9:03 a.m.
NED2 Entity disambiguation (via description) batch_6a423629cebc8190ac85a6a7dc22a1bf completed June 29, 2026, 9:08 a.m.
Created at: May 3, 2026, 4:31 p.m.