Triple

T21256230
Position Surface form Disambiguated ID Type / Status
Subject Waynesville, Ohio E523876 entity
Predicate roadJunctionOf P6234 FINISHED
Object State Route 42
State Route 42 is a state highway in Ohio that serves as a key regional route connecting several towns and communities, including Waynesville.
 E2286420 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 42 | Statement: [Waynesville, Ohio, roadJunctionOf, State Route 42]
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 42
Triple: [Waynesville, Ohio, roadJunctionOf, State Route 42]
Generated description
State Route 42 is a state highway in Ohio that serves as a key regional route connecting several towns and communities, including Waynesville.

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_69e0b5146c108190adc9adb73e90abff completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e735a1f9e08190b494f582bcae5c35 completed April 21, 2026, 8:30 a.m.
NED1 Entity disambiguation (via context triple) batch_6a46b209b54881909c76c6422006fa5b completed July 2, 2026, 6:46 p.m.
NEDg Description generation batch_6a46b5ffedb0819094cacbf6dcadf43f completed July 2, 2026, 7:03 p.m.
NED2 Entity disambiguation (via description) batch_6a46b66942d08190ab2595c47901f2d0 completed July 2, 2026, 7:05 p.m.
Created at: April 16, 2026, 3:58 p.m.