Triple

T16893030
Position Surface form Disambiguated ID Type / Status
Subject Sts. Peter and Paul Church, Panevėžys E424222 entity
Predicate locatedIn P40 FINISHED
Object Panevėžys E94346 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: Panevėžys | Statement: [Sts. Peter and Paul Church, Panevėžys, locatedIn, Panevėžys]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Panevėžys
Context triple: [Sts. Peter and Paul Church, Panevėžys, locatedIn, Panevėžys]
  • A. Panevėžys chosen
    Panevėžys is a major city in northern Lithuania known as an important regional industrial and cultural center.
  • B. Vilkaviškis
    Vilkaviškis is a town in southwestern Lithuania known as an administrative and historical center of the surrounding agricultural region.
  • C. Kaunas
    Kaunas is the second-largest city in Lithuania, known as a historic cultural and academic center located at the confluence of the Nemunas and Neris rivers.
  • D. Zarasai
    Zarasai is a small town in northeastern Lithuania known for its lakes and scenic natural surroundings.
  • E. Radviliškis
    Radviliškis is a town in northern Lithuania known as a regional railway hub and administrative center within Šiauliai County.
  • 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_69d889da3e8c8190a2b118f383f0beac completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e3bbc6b97c8190b18aca477d6ef647 completed April 18, 2026, 5:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a015fb6aff481908cb3d03b229ebf11 completed May 11, 2026, 4:48 a.m.
Created at: April 10, 2026, 5:29 a.m.