Triple

T24421037
Position Surface form Disambiguated ID Type / Status
Subject Kiryat Luza E615722 entity
Predicate hasEducationCharacteristic P155861 FINISHED
Object children often attend schools in nearby areas 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: children often attend schools in nearby areas | Statement: [Kiryat Luza, hasEducationCharacteristic, children often attend schools in nearby areas]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasEducationCharacteristic
Context triple: [Kiryat Luza, hasEducationCharacteristic, children often attend schools in nearby areas]
  • A. educationLevelCharacteristic
    Indicates that one entity specifies, describes, or constrains the education level associated with another entity.
  • B. hasEducationIn
    Indicates that an entity has received education, training, or formal study in a specified field, subject, or discipline.
  • C. hasEducationalStatus
    Indicates that an entity possesses a particular level, state, or condition of formal education or academic attainment.
  • D. educationStatus
    Indicates the current or achieved level, stage, or condition of an entity’s formal education.
  • E. educatedAt
    Indicates that an entity received education or formal training at a specified institution or place of learning.
  • 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_69e2d7eadb248190a867130fe45f0388 completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f296a34d3081908d6099365e2e4046 completed April 29, 2026, 11:39 p.m.
PD Predicate disambiguation batch_69f287cc4fd4819081e93cc638d9512d completed April 29, 2026, 10:35 p.m.
PDg Predicate description generation batch_69f2915233c48190a181c8c1924e892c completed April 29, 2026, 11:16 p.m.
Created at: April 18, 2026, 2:14 a.m.