Triple

T27720581
Position Surface form Disambiguated ID Type / Status
Subject Wisconsin Dells, Wisconsin E698940 entity
Predicate transportation P230 FINISHED
Object Wisconsin Dells station
Wisconsin Dells station is a passenger rail station in Wisconsin Dells, Wisconsin, serving as a stop for Amtrak trains and a gateway for tourists visiting the region’s popular waterparks and attractions.
E1784987 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: Wisconsin Dells station | Statement: [Wisconsin Dells, Wisconsin, transportation, Wisconsin Dells station]
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: Wisconsin Dells station
Triple: [Wisconsin Dells, Wisconsin, transportation, Wisconsin Dells station]
Generated description
Wisconsin Dells station is a passenger rail station in Wisconsin Dells, Wisconsin, serving as a stop for Amtrak trains and a gateway for tourists visiting the region’s popular waterparks and attractions.

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_69ef591012dc8190a6f1ec994f9f7ff7 completed April 27, 2026, 12:39 p.m.
NER Named-entity recognition batch_69f6363b50f08190a0d8e16d748388f5 completed May 2, 2026, 5:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12e4683d9c8190958bec8efb9eee93 completed May 24, 2026, 11:43 a.m.
NEDg Description generation batch_6a12e4db50808190a744e3854a3dda1d completed May 24, 2026, 11:45 a.m.
NED2 Entity disambiguation (via description) batch_6a12e540c20881908afe4a94e82ef68a completed May 24, 2026, 11:47 a.m.
Created at: April 27, 2026, 3:06 p.m.