Triple

T12102109
Position Surface form Disambiguated ID Type / Status
Subject Burggarten E288215 entity
Predicate adjacentTo P224 FINISHED
Object Albertina E854856 NE 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: Albertina | Statement: [Burggarten, adjacentTo, Albertina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Albertina
Context triple: [Burggarten, adjacentTo, Albertina]
  • A. Albertina
    Albertina was the historic University of Königsberg, a prominent Prussian center of learning and research founded in the 16th century.
  • B. Albertina chosen
    Albertina is a renowned art museum and graphic arts collection in Vienna, Austria, famous for its vast holdings of prints and drawings by masters such as Dürer, Michelangelo, and Picasso.
  • C. Antoinette
    Antoinette is a feminine given name of French origin, historically associated with nobility and later borne by various notable figures in the arts and public life.
  • D. Antoinette
    Antoinette is the birth name of Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
  • E. Catharina
    Catharina of Württemberg was a 19th-century German princess who became Queen consort of Westphalia through her marriage to Jérôme Bonaparte, Napoleon’s youngest brother.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69d6ab4a5c448190a110d1273314b21a completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d9155576b48190849f0c3e079a935f completed April 10, 2026, 3:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f674eab481909859b5403741eddd completed May 2, 2026, 1:04 p.m.
Created at: April 8, 2026, 9:48 p.m.