Triple

T21357500
Position Surface form Disambiguated ID Type / Status
Subject Pennsylvania Route 120 E526671 entity
Predicate isNumberedAfter P4527 FINISHED
Object Pennsylvania Route 119
Pennsylvania Route 119 is a state highway in western Pennsylvania that runs generally north–south, connecting communities such as Connellsville, Greensburg, and Indiana.
E1692329 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 119 | Statement: [Pennsylvania Route 120, isNumberedAfter, Pennsylvania Route 119]
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 119
Triple: [Pennsylvania Route 120, isNumberedAfter, Pennsylvania Route 119]
Generated description
Pennsylvania Route 119 is a state highway in western Pennsylvania that runs generally north–south, connecting communities such as Connellsville, Greensburg, and Indiana.

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_69e0b51d8a308190b09113b3b3f9bc15 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e8afa2d11c81908608851940e4e6d3 completed April 22, 2026, 11:23 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10cb9f271881909c6b0cab56f96423 completed May 22, 2026, 9:33 p.m.
NEDg Description generation batch_6a10cc4b6a148190bd5e4f15b4865bd6 completed May 22, 2026, 9:36 p.m.
NED2 Entity disambiguation (via description) batch_6a10ccc0f98081908f4819dd1f61c492 completed May 22, 2026, 9:38 p.m.
Created at: April 16, 2026, 5:07 p.m.