Triple

T20186019
Position Surface form Disambiguated ID Type / Status
Subject Rocca al Mare E492862 entity
Predicate hasEducationalInstitution P113 FINISHED
Object Rocca al Mare School NE NERFINISHED

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: Rocca al Mare School | Statement: [Rocca al Mare, hasEducationalInstitution, Rocca al Mare School]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rocca al Mare School
Context triple: [Rocca al Mare, hasEducationalInstitution, Rocca al Mare School]
  • A. Rocca al Mare School chosen
    Rocca al Mare School is a prominent private educational institution in Tallinn, Estonia, known for its modern learning environment and strong academic reputation.
  • B. Mambrino School
    Mambrino School is a public school serving students in the Granbury Independent School District in Granbury, Texas.
  • C. Magnaura School
    Magnaura School was a prestigious educational institution in Constantinople associated with the Byzantine imperial court and higher learning.
  • D. Diomede School
    Diomede School is a small public K–12 school serving the remote island community of Diomede, Alaska, in the Bering Strait.
  • E. Michelago Public School
    Michelago Public School is a small rural primary school serving the local community of Michelago, New South Wales, Australia.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69da6268a034819081cbd9ea5a1c9475 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e668f2c03c819096336462f59dba91 completed April 20, 2026, 5:57 p.m.
Created at: April 11, 2026, 11:36 p.m.