Triple

T28358430
Position Surface form Disambiguated ID Type / Status
Subject South Korean national highway network E718299 entity
Predicate hasPart P35 FINISHED
Object National Route 50
National Route 50 is a major east–west highway in South Korea that connects key cities and regions as part of the national road network.
 E1837239 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: National Route 50 | Statement: [South Korean national highway network, hasPart, National Route 50]
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: National Route 50
Triple: [South Korean national highway network, hasPart, National Route 50]
Generated description
National Route 50 is a major east–west highway in South Korea that connects key cities and regions as part of the national road network.

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_69eff6ec27b481908c8d7b86c47893d9 completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64c2f74b08190b7b6f4f8e82388a8 completed May 2, 2026, 7:10 p.m.
NED1 Entity disambiguation (via context triple) batch_6a24bb7daf2081909b4f39bf8e469801 completed June 7, 2026, 12:29 a.m.
NEDg Description generation batch_6a24c6be99c88190bc453b178830534f completed June 7, 2026, 1:17 a.m.
NED2 Entity disambiguation (via description) batch_6a24ca97cbcc81909259460b11b2df9a completed June 7, 2026, 1:34 a.m.
Created at: April 28, 2026, 12:50 a.m.