Triple

T1173265
Position Surface form Disambiguated ID Type / Status
Subject Japonic languages E24960 entity
Predicate arealRelation P24620 FINISHED
Object in contact with Koreanic languages 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: in contact with Koreanic languages | Statement: [Japonic languages, arealRelation, in contact with Koreanic languages]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: arealRelation
Context triple: [Japonic languages, arealRelation, in contact with Koreanic languages]
  • A. spatialRelation
    Indicates a spatial relationship between entities, specifying how one is positioned or located relative to another in space.
  • B. arealFeature
    Indicates a relationship where something is characterized as a spatial or geographic feature occupying an area on a surface or map.
  • C. datumRelation
    Indicates a relationship where one piece of data is connected to, derived from, or otherwise associated with another piece of data.
  • D. typeOfMunicipalRelationship
    Indicates a formal type or category of administrative or cooperative relationship that exists between municipalities.
  • E. representsSubdivisionOf
    Indicates that one administrative or territorial unit is a smaller, constituent part of a larger administrative or territorial unit.
  • F. None of above. chosen

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_69a494082a7c819095004f423f294a64 completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bcecab688190b21a926874cd98d1 completed March 1, 2026, 10:25 p.m.
PD Predicate disambiguation batch_69a4bb5656948190b0b1d5446ad06005 completed March 1, 2026, 10:19 p.m.
PDg Predicate description generation batch_69a4bbd7ff1881908c943ecdfea59e81 completed March 1, 2026, 10:21 p.m.
Created at: March 1, 2026, 7:45 p.m.