Triple

T27260734
Position Surface form Disambiguated ID Type / Status
Subject Anson County E687753 entity
Predicate borders P224 FINISHED
Object Richmond County, South Carolina
Richmond County, South Carolina is a county-level jurisdiction in the U.S. state of South Carolina known for its rural communities and location in the eastern part of the state.
E2133333 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: Richmond County, South Carolina | Statement: [Anson County, borders, Richmond County, South Carolina]
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: Richmond County, South Carolina
Triple: [Anson County, borders, Richmond County, South Carolina]
Generated description
Richmond County, South Carolina is a county-level jurisdiction in the U.S. state of South Carolina known for its rural communities and location in the eastern part of the state.

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_69ef3557abc481908bf3c146f0f3356a completed April 27, 2026, 10:07 a.m.
NER Named-entity recognition batch_69f626ee531c81908f23232136023c66 completed May 2, 2026, 4:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a380f7f9ebc8190a856e9b3052a7108 completed June 21, 2026, 4:21 p.m.
NEDg Description generation batch_6a38107dc6e481908b57199adbcc20a6 completed June 21, 2026, 4:25 p.m.
NED2 Entity disambiguation (via description) batch_6a3811e5b0d88190bc0f5cebe83b3768 completed June 21, 2026, 4:31 p.m.
Created at: April 27, 2026, 10:52 a.m.