Triple

T3189112
Position Surface form Disambiguated ID Type / Status
Subject Mishima E66773 entity
Predicate hasRoad P959 FINISHED
Object National Route 1 E141720 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 1 | Statement: [Mishima, hasRoad, National Route 1]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: National Route 1
Context triple: [Mishima, hasRoad, National Route 1]
  • A. National Route 1 chosen
    National Route 1 is a major Japanese highway that links Tokyo with Osaka, passing through key urban and industrial areas along the Pacific coast of Honshu.
  • B. National Route 1A
    National Route 1A is Vietnam’s primary north–south highway, running the length of the country and connecting major cities, provinces, and economic regions.
  • C. National Route 17
    National Route 17 is a major South Korean highway that runs north–south across the country, connecting multiple cities and regions including Eumseong County.
  • D. National Route 3
    National Route 3 is a major Argentine highway that runs along the Atlantic coast of Patagonia, connecting Buenos Aires with key southern cities including Comodoro Rivadavia.
  • E. National Route 3
    National Route 3 is a major South Korean highway that runs north–south, connecting multiple regions and cities across the country.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ad8587c1bc8190a2595f2c22ee1001 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada6e491508190bc881feaee3889bc completed March 8, 2026, 4:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69b24b9a9bc88190b7090bda8fe6260c completed March 12, 2026, 5:14 a.m.
Created at: March 8, 2026, 3:06 p.m.