Triple

T37133746
Position Surface form Disambiguated ID Type / Status
Subject Tannersville, Pennsylvania E919598 entity
Predicate hasMajorRoad P385 FINISHED
Object Pennsylvania Route 715
Pennsylvania Route 715 is a state highway in northeastern Pennsylvania that serves as a key connector through Monroe County, including the Tannersville area.
E2292472 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 715 | Statement: [Tannersville, Pennsylvania, hasMajorRoad, Pennsylvania Route 715]
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 715
Triple: [Tannersville, Pennsylvania, hasMajorRoad, Pennsylvania Route 715]
Generated description
Pennsylvania Route 715 is a state highway in northeastern Pennsylvania that serves as a key connector through Monroe County, including the Tannersville 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_69f76e9d13e48190a108f7fbf80ff375 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fb303fd50c8190bc6eccfe05a3e206 completed May 6, 2026, 12:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a799969af6881908fe2ef003d3e30ee completed Aug. 10, 2026, 9:27 a.m.
NEDg Description generation batch_6a799a4dda9c81908d11d515d35c6d48 completed Aug. 10, 2026, 9:30 a.m.
NED2 Entity disambiguation (via description) batch_6a799a9f2f00819098677733c659afe4 completed Aug. 10, 2026, 9:32 a.m.
Created at: May 3, 2026, 4:15 p.m.