Triple

T16027448
Position Surface form Disambiguated ID Type / Status
Subject Maria of Tver E388752 entity
Predicate title P38 FINISHED
Object Princess of Tver E1189224 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: Princess of Tver | Statement: [Maria of Tver, title, Princess of Tver]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Princess of Tver
Context triple: [Maria of Tver, title, Princess of Tver]
  • A. Princess of Tver chosen
    Princess of Tver was a medieval Russian princely title held by female members of the ruling dynasty of the Principality of Tver.
  • B. Princess of Ryazan
    Princess of Ryazan was a medieval Rus’ noble title held by the ruling princess consort of the Principality of Ryazan, a regional power in what is now central Russia.
  • C. Princess of Moscow
    Princess of Moscow was a medieval Russian noble title held by the wife or female consort of the ruling Prince of Moscow, associated with the Muscovite royal court.
  • D. Princess of Borovsk
    Princess of Borovsk is a Russian princely title historically associated with the medieval noblewoman Maria of Borovsk and the ruling family of the Borovsk principality.
  • E. Princess Dragomiroff
    Princess Dragomiroff is an elderly, imperious Russian aristocrat who appears as a key suspect in Agatha Christie’s detective novel "Murder on the Orient Express."
  • 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_69d86dada3808190825d5f80d72fbe88 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e183294984819080b8727a3511a21b completed April 17, 2026, 12:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffe47280448190923a36e9a41ce7bc completed May 10, 2026, 1:50 a.m.
Created at: April 10, 2026, 4:56 a.m.