Triple

T24375397
Position Surface form Disambiguated ID Type / Status
Subject City of Avondale Estates government E614457 entity
Predicate meetsAt P373 FINISHED
Object Avondale Estates City Hall
Avondale Estates City Hall is the primary municipal building and administrative center for the City of Avondale Estates, Georgia.
E1631252 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: Avondale Estates City Hall | Statement: [City of Avondale Estates government, meetsAt, Avondale Estates City Hall]
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: Avondale Estates City Hall
Triple: [City of Avondale Estates government, meetsAt, Avondale Estates City Hall]
Generated description
Avondale Estates City Hall is the primary municipal building and administrative center for the City of Avondale Estates, 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_69e2d7e1e010819098b95eb3f905943d completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f293d73c0c8190b80e257845a04c57 completed April 29, 2026, 11:27 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0fd678ee848190a3c4cae2749997c0 completed May 22, 2026, 4:07 a.m.
NEDg Description generation batch_6a0fd7f84a908190a128494e4b442ada completed May 22, 2026, 4:13 a.m.
NED2 Entity disambiguation (via description) batch_6a0fd8d0f6848190a77aff96b4fbcc3d completed May 22, 2026, 4:17 a.m.
Created at: April 18, 2026, 2:02 a.m.