Triple

T5158910
Position Surface form Disambiguated ID Type / Status
Subject Shimonoseki E116384 entity
Predicate isPortForFerriesTo P1737 FINISHED
Object Busan E4279 NE FINISHED

How this triple was built (3 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: Busan | Statement: [Shimonoseki, isPortForFerriesTo, Busan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Busan
Context triple: [Shimonoseki, isPortForFerriesTo, Busan]
  • A. Busan chosen
    Busan is South Korea’s second-largest city and a major international port known for its bustling harbor, beaches, and coastal scenery.
  • B. Ulsan
    Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
  • C. Incheon
    Incheon is a major port city in northwestern South Korea, known for its international airport and role as a key transportation and economic hub.
  • D. Daegu
    Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
  • E. Gunsan
    Gunsan is a coastal city in North Jeolla Province, South Korea, known for its port, industrial facilities, and longstanding association with nearby military air operations.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: isPortForFerriesTo
Context triple: [Shimonoseki, isPortForFerriesTo, Busan]
  • A. hasFerryPort
    Indicates that a place serves as a location where ferries regularly dock to load and unload passengers or cargo.
  • B. hasFerryService chosen
    Indicates that there is an operational ferry connection or transport service available between the related locations or entities.
  • C. hasNearbyFerryPort
    Indicates that one location is situated close enough to another location that serves as a ferry port to be considered nearby.
  • D. eraOfMajorUseAsFerryTerminal
    Indicates the time period during which a location was primarily used as a ferry terminal.
  • E. hasFerryType
    Indicates that an entity (such as a ferry route or service) is associated with a specific type or category of ferry.
  • F. None of above.

Provenance (4 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_69bd445edb3881909b93b34d260717fc completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd79c1354c81908176703b4853c1a4 completed March 20, 2026, 4:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69bee07283c08190a8fc23d3041275ee completed March 21, 2026, 6:16 p.m.
PD Predicate disambiguation batch_69bd77b0fbb88190851e2d7ae1bdcc09 completed March 20, 2026, 4:37 p.m.
Created at: March 20, 2026, 1:44 p.m.