Triple

T8407278
Position Surface form Disambiguated ID Type / Status
Subject Cao Rui E198530 entity
Predicate templeName P44027 FINISHED
Object Liezu E710005 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: Liezu | Statement: [Cao Rui, templeName, Liezu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Liezu
Context triple: [Cao Rui, templeName, Liezu]
  • A. Liezu chosen
    Liezu is the honorific temple name bestowed upon Liu Bei, the founding emperor of the Shu Han state during China’s Three Kingdoms period.
  • B. Oppeln
    Oppeln is the historical German name for the city of Opole, a major cultural and administrative center in southwestern Poland’s Silesia region.
  • C. Görlitz
    Görlitz is a historic city in eastern Germany on the Lusatian Neisse River, known for its well-preserved old town and role as a popular film location.
  • D. Frankfurt (Oder)
    Frankfurt (Oder) is a German city on the Oder River at the Polish border, known as a historic university and trade center in the state of Brandenburg.
  • E. Lankwitz
    Lankwitz is a residential locality in the southwestern part of Berlin, known for its quiet neighborhoods, green spaces, and mix of historic and modern architecture.
  • 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_69ca8310df9c8190b25f16161cca3e41 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cb831409308190981089c303ebaef4 completed March 31, 2026, 8:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69cffd4c307881909996adedc959180f completed April 3, 2026, 5:47 p.m.
Created at: March 30, 2026, 6:05 p.m.