Triple

T28358473
Position Surface form Disambiguated ID Type / Status
Subject South Korean national highway network E718299 entity
Predicate hasPart P35 FINISHED
Object National Route 323
National Route 323 is a designated national highway in South Korea that connects regional cities and towns as part of the country’s primary road network.
 E2002685 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 323 | Statement: [South Korean national highway network, hasPart, National Route 323]
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 323
Triple: [South Korean national highway network, hasPart, National Route 323]
Generated description
National Route 323 is a designated national highway in South Korea that connects regional cities and towns as part of the country’s primary 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_6a3056d7b7908190bfec44693723db23 completed June 15, 2026, 7:47 p.m.
NEDg Description generation batch_6a31af0dd4b48190be2aa9c952a9aff6 completed June 16, 2026, 8:16 p.m.
NED2 Entity disambiguation (via description) batch_6a31bab61f508190a4bfde1478397f6c completed June 16, 2026, 9:05 p.m.
Created at: April 28, 2026, 12:50 a.m.