Triple

T25036164
Position Surface form Disambiguated ID Type / Status
Subject Georgia municipal courts E626982 entity
Predicate governedBy P46 FINISHED
Object Georgia Municipal Courts Act
The Georgia Municipal Courts Act is a state law that establishes the structure, powers, and procedures of municipal courts throughout Georgia.
 E1663112 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 Municipal Courts Act | Statement: [Georgia municipal courts, governedBy, Georgia Municipal Courts Act]
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 Municipal Courts Act
Triple: [Georgia municipal courts, governedBy, Georgia Municipal Courts Act]
Generated description
The Georgia Municipal Courts Act is a state law that establishes the structure, powers, and procedures of municipal courts throughout Georgia.

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_69e2ff2a2c088190be513727ee8bfe78 completed April 18, 2026, 3:48 a.m.
NER Named-entity recognition batch_69f4530674d88190a3516bbd64234111 completed May 1, 2026, 7:15 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1048c008688190b8cf55ab96b2268c completed May 22, 2026, 12:14 p.m.
NEDg Description generation batch_6a104a6d40f88190941fae4e53c175f7 completed May 22, 2026, 12:22 p.m.
NED2 Entity disambiguation (via description) batch_6a104c2d8308819097b21b979944585e completed May 22, 2026, 12:29 p.m.
Created at: April 18, 2026, 6:08 a.m.