Triple

T32227523
Position Surface form Disambiguated ID Type / Status
Subject Ritenour School District E823245 entity
Predicate hasElementarySchool P113 FINISHED
Object Marvin Elementary School
Marvin Elementary School is a public elementary school serving young students and families within the Ritenour School District in Missouri.
E2003320 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: Marvin Elementary School | Statement: [Ritenour School District, hasElementarySchool, Marvin Elementary School]
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: Marvin Elementary School
Triple: [Ritenour School District, hasElementarySchool, Marvin Elementary School]
Generated description
Marvin Elementary School is a public elementary school serving young students and families within the Ritenour School District in Missouri.

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_69f3490b4f948190b99e4f999f5be25f completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6bbcca23c8190913df3c818b52fbb completed May 3, 2026, 3:06 a.m.
NED1 Entity disambiguation (via context triple) batch_6a33e88ada5081908dc960740821ba0b completed June 18, 2026, 12:46 p.m.
NEDg Description generation batch_6a33e9fc4d988190bebe1f53e43dc08c completed June 18, 2026, 12:52 p.m.
NED2 Entity disambiguation (via description) batch_6a341ee589688190b72cbf554edae693 completed June 18, 2026, 4:37 p.m.
Created at: May 1, 2026, 12:38 a.m.