Triple

T27999112
Position Surface form Disambiguated ID Type / Status
Subject Slovak Rome E707097 entity
Predicate appliesToCityWithFeature P78765 FINISHED
Object high density of churches per area 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 density of churches per area | Statement: [Slovak Rome, appliesToCityWithFeature, high density of churches per area]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: appliesToCityWithFeature
Context triple: [Slovak Rome, appliesToCityWithFeature, high density of churches per area]
  • A. hasUrbanFeature
    Indicates that a place or area possesses a specific urban element or infrastructure feature (such as roads, parks, or buildings) as part of its built environment.
  • B. coversCity
    Indicates that one entity extends over, includes, or geographically encompasses the area of a specified city.
  • C. refersToCityWithAttribute chosen
    Indicates that one entity refers to a city that possesses a specified attribute or set of attributes.
  • D. supportsCity
    Indicates that one entity provides assistance, resources, or backing to a city, helping it function, develop, or achieve its goals.
  • E. appliesToUrbanArea
    Indicates that the relationship, rule, or condition is specifically relevant or applicable to an urban area.
  • 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_69ef96b980d88190a753b2f9a978595a completed April 27, 2026, 5:02 p.m.
NER Named-entity recognition batch_69fbc9d1dba881908c399b8e1dc13ce2 completed May 6, 2026, 11:08 p.m.
PD Predicate disambiguation batch_69fbc8ec03ac8190a757563f96fab283 completed May 6, 2026, 11:04 p.m.
Created at: April 27, 2026, 7:55 p.m.