Triple

T28358407
Position Surface form Disambiguated ID Type / Status
Subject South Korean national highway network E718299 entity
Predicate hasPart P35 FINISHED
Object National Route 22
National Route 22 is a major South Korean highway that connects key cities and regions as part of the country's national road network.
 E1825519 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 22 | Statement: [South Korean national highway network, hasPart, National Route 22]
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 22
Triple: [South Korean national highway network, hasPart, National Route 22]
Generated description
National Route 22 is a major South Korean highway that connects key cities and regions as part of the country's 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_6a1cb6cd2d948190a5e5b2766eec16ae completed May 31, 2026, 10:31 p.m.
NEDg Description generation batch_6a1cba3512c48190a428357ab312fde1 completed May 31, 2026, 10:46 p.m.
NED2 Entity disambiguation (via description) batch_6a1cbacbae2081909e0d7bd825a49306 completed May 31, 2026, 10:48 p.m.
Created at: April 28, 2026, 12:50 a.m.