Triple

T26264722
Position Surface form Disambiguated ID Type / Status
Subject Town of Groton, New York E656947 entity
Predicate hasSchoolDistrict P226 FINISHED
Object Groton Central School District
Groton Central School District is a public school district serving students in the rural community of Groton in upstate New York.
E1719719 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: Groton Central School District | Statement: [Town of Groton, New York, hasSchoolDistrict, Groton Central 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: Groton Central School District
Triple: [Town of Groton, New York, hasSchoolDistrict, Groton Central School District]
Generated description
Groton Central School District is a public school district serving students in the rural community of Groton in upstate New York.

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_69ee5b4e21bc819082be98bc9ab09796 completed April 26, 2026, 6:37 p.m.
NER Named-entity recognition batch_69f60e03746c81909c28c427b9754c06 completed May 2, 2026, 2:45 p.m.
NED1 Entity disambiguation (via context triple) batch_6a118fb07d8c81908eaac99900e6ba2b completed May 23, 2026, 11:29 a.m.
NEDg Description generation batch_6a119053e3b0819092c8e62b5b4ae02a completed May 23, 2026, 11:32 a.m.
NED2 Entity disambiguation (via description) batch_6a1190db5ab48190a5b902fee03abdde completed May 23, 2026, 11:34 a.m.
Created at: April 26, 2026, 9:11 p.m.