Triple

T21369977
Position Surface form Disambiguated ID Type / Status
Subject Brunswick, Ohio E527027 entity
Predicate majorRoad P385 FINISHED
Object State Route 303
State Route 303 is a primary east–west state highway in northern Ohio that serves as a key thoroughfare through communities such as Brunswick.
 E2287377 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: State Route 303 | Statement: [Brunswick, Ohio, majorRoad, State Route 303]
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: State Route 303
Triple: [Brunswick, Ohio, majorRoad, State Route 303]
Generated description
State Route 303 is a primary east–west state highway in northern Ohio that serves as a key thoroughfare through communities such as Brunswick.

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_69e0b51e80808190ba5cb05667af02a9 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e8b0adbbc081908ed4d839f7cf8f38 completed April 22, 2026, 11:27 a.m.
NED1 Entity disambiguation (via context triple) batch_6a47864334008190b4245d660ce3fbc6 completed July 3, 2026, 9:52 a.m.
NEDg Description generation batch_6a478753162c8190bee5906d474c2949 completed July 3, 2026, 9:56 a.m.
NED2 Entity disambiguation (via description) batch_6a4787f3c5ec8190aa5bdb70980ff559 completed July 3, 2026, 9:59 a.m.
Created at: April 16, 2026, 5:10 p.m.