Triple

T26441338
Position Surface form Disambiguated ID Type / Status
Subject Old Fields, West Virginia E665096 entity
Predicate county P75 FINISHED
Object Hardy County
Hardy County is a rural county in eastern West Virginia known for its Appalachian landscapes, agriculture, and small communities near the South Branch Potomac River.
E1789394 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: Hardy County | Statement: [Old Fields, West Virginia, county, Hardy County]
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: Hardy County
Triple: [Old Fields, West Virginia, county, Hardy County]
Generated description
Hardy County is a rural county in eastern West Virginia known for its Appalachian landscapes, agriculture, and small communities near the South Branch Potomac River.

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_69ee883c851881909e2ab04efbb3c5fe completed April 26, 2026, 9:48 p.m.
NER Named-entity recognition batch_69f6121bf448819088a5516943658eff completed May 2, 2026, 3:02 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12ec8844f081909f767e6a06aef37b completed May 24, 2026, 12:18 p.m.
NEDg Description generation batch_6a12ed678580819082d28135e3fcb818 completed May 24, 2026, 12:21 p.m.
NED2 Entity disambiguation (via description) batch_6a12eef15e2c819099088626fb78adce completed May 24, 2026, 12:28 p.m.
Created at: April 26, 2026, 11:58 p.m.