Triple

T25147029
Position Surface form Disambiguated ID Type / Status
Subject Oley, Pennsylvania E629962 entity
Predicate hasSchoolDistrict P226 FINISHED
Object Oley Valley School District
Oley Valley School District is a public school district serving students in and around the rural community of Oley in Berks County, Pennsylvania.
E1667895 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: Oley Valley School District | Statement: [Oley, Pennsylvania, hasSchoolDistrict, Oley Valley School District]
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: Oley Valley School District
Triple: [Oley, Pennsylvania, hasSchoolDistrict, Oley Valley School District]
Generated description
Oley Valley School District is a public school district serving students in and around the rural community of Oley in Berks County, Pennsylvania.

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_69e2ff349e408190a6f4a5a66279f54d completed April 18, 2026, 3:49 a.m.
NER Named-entity recognition batch_69f4684d3d748190938bfad83030d460 completed May 1, 2026, 8:46 a.m.
NED1 Entity disambiguation (via context triple) batch_6a105d06b3248190916843aa59dbcda6 completed May 22, 2026, 1:41 p.m.
NEDg Description generation batch_6a105e17b4708190bceee2e6c3f4f3a7 completed May 22, 2026, 1:45 p.m.
NED2 Entity disambiguation (via description) batch_6a105f91c8808190b902d606e0ad6d0e completed May 22, 2026, 1:52 p.m.
Created at: April 18, 2026, 6:30 a.m.