Triple

T30891607
Position Surface form Disambiguated ID Type / Status
Subject Essex, Maryland E786912 entity
Predicate hasEducationalInstitution P113 FINISHED
Object Deep Creek Middle School
Deep Creek Middle School is a public middle school serving students in the Essex community of Baltimore County, Maryland.
 E1937104 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: Deep Creek Middle School | Statement: [Essex, Maryland, hasEducationalInstitution, Deep Creek Middle 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: Deep Creek Middle School
Triple: [Essex, Maryland, hasEducationalInstitution, Deep Creek Middle School]
Generated description
Deep Creek Middle School is a public middle school serving students in the Essex community of Baltimore County, Maryland.

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_69f224bbfa7c81908448e0c261c523e3 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f69209a11481909706ec291ac73e6b completed May 3, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_6a28c7e92f788190a5219487c3aea907 completed June 10, 2026, 2:11 a.m.
NEDg Description generation batch_6a28cb3aa04c8190a1000c0ad3c9f675 completed June 10, 2026, 2:26 a.m.
NED2 Entity disambiguation (via description) batch_6a28cc6d10c48190b4d80bb129c95229 completed June 10, 2026, 2:31 a.m.
Created at: April 29, 2026, 8:49 p.m.