Triple

T26040717
Position Surface form Disambiguated ID Type / Status
Subject Georgia Code E647681 entity
Predicate differsFrom P278 FINISHED
Object Georgia Administrative Code
The Georgia Administrative Code is the compilation of rules and regulations issued by Georgia’s state agencies that implement and interpret the state’s statutes.
 E1708712 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: Georgia Administrative Code | Statement: [Georgia Code, differsFrom, Georgia Administrative Code]
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: Georgia Administrative Code
Triple: [Georgia Code, differsFrom, Georgia Administrative Code]
Generated description
The Georgia Administrative Code is the compilation of rules and regulations issued by Georgia’s state agencies that implement and interpret the state’s statutes.

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_69e77e8c88f08190858c4c81bd2e1b9a completed April 21, 2026, 1:41 p.m.
NER Named-entity recognition batch_69f60622ddf48190b95318ea7a3676ce completed May 2, 2026, 2:11 p.m.
NED1 Entity disambiguation (via context triple) batch_6a111b198da88190b07efa313c80a974 completed May 23, 2026, 3:12 a.m.
NEDg Description generation batch_6a111e76e474819087a914d13e5df465 completed May 23, 2026, 3:26 a.m.
NED2 Entity disambiguation (via description) batch_6a111ec8b2b48190ab279139c0dbee15 completed May 23, 2026, 3:28 a.m.
Created at: April 22, 2026, 9:08 a.m.