Triple

T34105321
Position Surface form Disambiguated ID Type / Status
Subject Park Rapids, Minnesota E874688 entity
Predicate hasAirport P105 FINISHED
Object Park Rapids Municipal Airport
Park Rapids Municipal Airport is a public-use airport serving general aviation needs for the city of Park Rapids in north-central Minnesota.
E2080911 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: Park Rapids Municipal Airport | Statement: [Park Rapids, Minnesota, hasAirport, Park Rapids Municipal Airport]
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: Park Rapids Municipal Airport
Triple: [Park Rapids, Minnesota, hasAirport, Park Rapids Municipal Airport]
Generated description
Park Rapids Municipal Airport is a public-use airport serving general aviation needs for the city of Park Rapids in north-central Minnesota.

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_69f349a80d4481908527317d43f5c579 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f70ca987dc81908adb7451a3e2c20f completed May 3, 2026, 8:51 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36ae659fc081908a1fa23595e3bfa8 completed June 20, 2026, 3:14 p.m.
NEDg Description generation batch_6a36aef70cac81909864ca6c04cbfa2a completed June 20, 2026, 3:17 p.m.
NED2 Entity disambiguation (via description) batch_6a36afc47fc4819097fd115ba24b55dc completed June 20, 2026, 3:20 p.m.
Created at: May 1, 2026, 1:53 a.m.