Triple

T35743581
Position Surface form Disambiguated ID Type / Status
Subject Redwood Falls, Minnesota E1033107 entity
Predicate hasAirport P105 FINISHED
Object Redwood Falls Municipal Airport
Redwood Falls Municipal Airport is a public-use airport serving the city of Redwood Falls and the surrounding region in southwestern Minnesota.
E2157524 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: Redwood Falls Municipal Airport | Statement: [Redwood Falls, Minnesota, hasAirport, Redwood Falls 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: Redwood Falls Municipal Airport
Triple: [Redwood Falls, Minnesota, hasAirport, Redwood Falls Municipal Airport]
Generated description
Redwood Falls Municipal Airport is a public-use airport serving the city of Redwood Falls and the surrounding region in southwestern 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_69f76e119d508190a3873cb302063832 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7a16d286881908544d7df6d3c7ea5 completed May 3, 2026, 7:26 p.m.
NED1 Entity disambiguation (via context triple) batch_6a389c0925c08190a23601486c988b19 completed June 22, 2026, 2:20 a.m.
NEDg Description generation batch_6a389c96694c8190869042074cd2f123 completed June 22, 2026, 2:23 a.m.
NED2 Entity disambiguation (via description) batch_6a389d7b23748190993070e1405d79de completed June 22, 2026, 2:27 a.m.
Created at: May 3, 2026, 4:06 p.m.