Triple

T27333294
Position Surface form Disambiguated ID Type / Status
Subject East Petersburg, Pennsylvania E689865 entity
Predicate transportationAccess P941 FINISHED
Object Pennsylvania Route 722
Pennsylvania Route 722 is a state highway in Lancaster County, Pennsylvania, serving as a local connector through suburban communities including the East Petersburg area.
E2246281 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: Pennsylvania Route 722 | Statement: [East Petersburg, Pennsylvania, transportationAccess, Pennsylvania Route 722]
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: Pennsylvania Route 722
Triple: [East Petersburg, Pennsylvania, transportationAccess, Pennsylvania Route 722]
Generated description
Pennsylvania Route 722 is a state highway in Lancaster County, Pennsylvania, serving as a local connector through suburban communities including the East Petersburg area.

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_69ef355e5b388190a8fc1eba9b4a6656 completed April 27, 2026, 10:07 a.m.
NER Named-entity recognition batch_69f62acd191481908212829e834fc980 completed May 2, 2026, 4:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a4103fc6b2481908d85a6d286b90923 completed June 28, 2026, 11:22 a.m.
NEDg Description generation batch_6a4104c79fb0819084b62acaa5ae7237 completed June 28, 2026, 11:25 a.m.
NED2 Entity disambiguation (via description) batch_6a41059ef42c81909a94722a1563fcd1 completed June 28, 2026, 11:29 a.m.
Created at: April 27, 2026, 11:39 a.m.