Triple

T31127521
Position Surface form Disambiguated ID Type / Status
Subject Sauk Centre, Minnesota E793399 entity
Predicate hasLandmark P105 FINISHED
Object Sinclair Lewis Park
Sinclair Lewis Park is a public recreational area in Sauk Centre, Minnesota, named in honor of the Nobel Prize–winning author Sinclair Lewis, who was born in the town.
E2126936 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: Sinclair Lewis Park | Statement: [Sauk Centre, Minnesota, hasLandmark, Sinclair Lewis 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: Sinclair Lewis Park
Triple: [Sauk Centre, Minnesota, hasLandmark, Sinclair Lewis Park]
Generated description
Sinclair Lewis Park is a public recreational area in Sauk Centre, Minnesota, named in honor of the Nobel Prize–winning author Sinclair Lewis, who was born in the town.

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_69f224d1701c819094f429798290e361 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f6973d98508190bb63caf9c1bdc2b1 completed May 3, 2026, 12:30 a.m.
NED1 Entity disambiguation (via context triple) batch_6a37d92b374481908468b52583d85265 completed June 21, 2026, 12:29 p.m.
NEDg Description generation batch_6a37da562654819080893c616ec257e2 completed June 21, 2026, 12:34 p.m.
NED2 Entity disambiguation (via description) batch_6a37dbed7540819081bd95af5520b163 completed June 21, 2026, 12:41 p.m.
Created at: April 29, 2026, 9:05 p.m.