Triple

T32951772
Position Surface form Disambiguated ID Type / Status
Subject Pennsylvania Route 93 E842974 entity
Predicate connectsToRoute P845 FINISHED
Object Pennsylvania Route 424
Pennsylvania Route 424 is a short state highway in Luzerne County that serves as a connector route near Hazleton, improving access between local roads and major regional corridors.
E2290264 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 424 | Statement: [Pennsylvania Route 93, connectsToRoute, Pennsylvania Route 424]
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 424
Triple: [Pennsylvania Route 93, connectsToRoute, Pennsylvania Route 424]
Generated description
Pennsylvania Route 424 is a short state highway in Luzerne County that serves as a connector route near Hazleton, improving access between local roads and major regional corridors.

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_69f3494a31f481909057136e49b4fe60 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6d143c38c8190a6076ae13f6c6a4c completed May 3, 2026, 4:38 a.m.
NED1 Entity disambiguation (via context triple) batch_6a5bb15744cc8190a0c5c7eedbe94582 completed July 18, 2026, 5:01 p.m.
NEDg Description generation batch_6a5bb1ea499c8190a3e5c29b14baec33 completed July 18, 2026, 5:03 p.m.
NED2 Entity disambiguation (via description) batch_6a5bb21ff0cc8190aa89eff1bd3396d0 completed July 18, 2026, 5:04 p.m.
Created at: May 1, 2026, 1:21 a.m.