Triple

T27264946
Position Surface form Disambiguated ID Type / Status
Subject Jung-gu E687873 entity
Predicate usedInCountryAdministrativeContext P8773 FINISHED
Object metropolitan city government LITERAL 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: metropolitan city government | Statement: [Jung-gu, usedInCountryAdministrativeContext, metropolitan city government]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: usedInCountryAdministrativeContext
Context triple: [Jung-gu, usedInCountryAdministrativeContext, metropolitan city government]
  • A. usedInCountryOrRegion
    Indicates that something (such as an item, concept, or practice) is utilized or applied within a specified country or region.
  • B. usedInCountry
    Indicates that something is utilized, applied, or in operation within the specified country.
  • C. appliesToAdministrativeTerritory chosen
    Indicates that something (such as a rule, measure, or status) is valid for, relevant to, or in force within a specific administrative territory.
  • D. usedInConstituentCountry
    Indicates that something is utilized or applied within a specific constituent country of a larger sovereign state.
  • E. usedForCountry
    Indicates that something is used for, or serves a purpose related to, a specific country.
  • F. None of above.

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_69ef3557abc481908bf3c146f0f3356a completed April 27, 2026, 10:07 a.m.
NER Named-entity recognition batch_69f71f8ee0688190bd025f27993452d3 completed May 3, 2026, 10:12 a.m.
PD Predicate disambiguation batch_69f71cc405c08190863565609a4c8499 completed May 3, 2026, 10 a.m.
Created at: April 27, 2026, 10:55 a.m.