Triple

T21808496
Position Surface form Disambiguated ID Type / Status
Subject Lochkov E538410 entity
Predicate partOf P40 FINISHED
Object Prague 16 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: Prague 16 | Statement: [Lochkov, partOf, Prague 16]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Prague 16
Context triple: [Lochkov, partOf, Prague 16]
  • A. Prague 16 chosen
    Prague 16 is a municipal district of Prague in the Czech Republic that includes neighborhoods such as Lochkov and forms part of the city’s southwestern administrative area.
  • B. Prague 17
    Prague 17 is a municipal district of Prague, Czech Republic, located on the western edge of the city and encompassing primarily residential neighborhoods.
  • C. Prague 1
    Prague 1 is the historic central district of Prague, encompassing many of the city’s most famous landmarks, government buildings, and tourist attractions.
  • D. Prague 10
    Prague 10 is one of the administrative districts of Prague, Czech Republic, encompassing mainly residential neighborhoods and parts of the city’s eastern area.
  • E. Prague 11
    Prague 11 is a municipal district in the southeastern part of Prague, Czech Republic, known largely for its extensive panel housing estates and residential neighborhoods such as Háje.
  • 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_69e0c473f0f8819086c9d1b4a143bd67 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69f078047ca88190a0efa4bc7f2faf80 completed April 28, 2026, 9:04 a.m.
Created at: April 16, 2026, 6:53 p.m.