Triple

T12547922
Position Surface form Disambiguated ID Type / Status
Subject Crawford County, Michigan E300020 entity
Predicate hasBorder P224 FINISHED
Object Montmorency County, Michigan
Montmorency County, Michigan is a sparsely populated county in the northern Lower Peninsula known for its forests, lakes, and outdoor recreation opportunities.
E1786540 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: Montmorency County, Michigan | Statement: [Crawford County, Michigan, hasBorder, Montmorency County, Michigan]
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: Montmorency County, Michigan
Triple: [Crawford County, Michigan, hasBorder, Montmorency County, Michigan]
Generated description
Montmorency County, Michigan is a sparsely populated county in the northern Lower Peninsula known for its forests, lakes, and outdoor recreation opportunities.

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_69d6ada707008190aaec1238117c9379 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d95481ba28819099f7cd2de02e8837 completed April 10, 2026, 7:50 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12e4247c6c81909e8cc1c969a80779 completed May 24, 2026, 11:42 a.m.
NEDg Description generation batch_6a12e4da65dc8190801cafed5fb95685 completed May 24, 2026, 11:45 a.m.
NED2 Entity disambiguation (via description) batch_6a12e5a642e4819095c21cfe6a85f12f completed May 24, 2026, 11:48 a.m.
Created at: April 8, 2026, 9:58 p.m.