Triple

T607702
Position Surface form Disambiguated ID Type / Status
Subject Finland E12029 entity
Predicate notableGeographicalCharacteristic P12436 FINISHED
Object high proportion of forests 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: high proportion of forests | Statement: [Finland, notableGeographicalCharacteristic, high proportion of forests]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: notableGeographicalCharacteristic
Context triple: [Finland, notableGeographicalCharacteristic, high proportion of forests]
  • A. hasGeographyCharacteristic chosen
    Indicates that an entity possesses a specific geographical feature, property, or attribute.
  • B. hasNaturalFeature
    Indicates that one entity possesses, contains, or is characterized by a particular natural feature (such as a mountain, river, forest, or coastline).
  • C. terrainFeature
    Indicates a relationship where one entity is a natural or constructed landform or surface characteristic associated with a given location or area.
  • D. politicalFeature
    Indicates that an entity possesses a political characteristic, attribute, or aspect relevant to governance, power structures, or public policy.
  • E. notablePlace
    Indicates that a place is especially significant, famous, or noteworthy in relation to the subject.
  • 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_69a493309df48190a327f748e88049a6 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49df34abc8190a578c8c2ab3d28e4 completed March 1, 2026, 8:13 p.m.
PD Predicate disambiguation batch_69a49cf8fc1c81908a9c7df552aa1a59 completed March 1, 2026, 8:09 p.m.
Created at: March 1, 2026, 7:35 p.m.