Triple

T28358434
Position Surface form Disambiguated ID Type / Status
Subject South Korean national highway network E718299 entity
Predicate hasPart P35 FINISHED
Object National Route 77
National Route 77 is a major South Korean highway that runs along much of the country’s western and southern coasts, connecting numerous coastal cities and regions.
 E1918045 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 77 | Statement: [South Korean national highway network, hasPart, National Route 77]
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 77
Triple: [South Korean national highway network, hasPart, National Route 77]
Generated description
National Route 77 is a major South Korean highway that runs along much of the country’s western and southern coasts, connecting numerous coastal cities and regions.

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_6a27abf4a9bc8190b5236993d9f55ff5 completed June 9, 2026, 6 a.m.
NEDg Description generation batch_6a27ae1a3784819097722737c0b949cb completed June 9, 2026, 6:09 a.m.
NED2 Entity disambiguation (via description) batch_6a27aef0046081908c95ec0fa0e0497d completed June 9, 2026, 6:13 a.m.
Created at: April 28, 2026, 12:50 a.m.