Triple

T12082946
Position Surface form Disambiguated ID Type / Status
Subject Prague 6 E287726 entity
Predicate borderedBy P224 FINISHED
Object Prague 1 E407427 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: Prague 1 | Statement: [Prague 6, borderedBy, Prague 1]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Prague 1
Context triple: [Prague 6, borderedBy, Prague 1]
  • A. Prague 1 chosen
    Prague 1 is the historic central district of Prague, encompassing many of the city’s most famous landmarks, government buildings, and tourist attractions.
  • B. Prague 9
    Prague 9 is a municipal district of Prague in the Czech Republic, known for its mix of residential areas, industrial zones, and major venues such as large sports and entertainment arenas.
  • C. Prague 6
    Prague 6 is a large municipal district of Prague, Czech Republic, known for its residential neighborhoods, diplomatic quarter, and proximity to Prague Castle and the airport.
  • D. Prague-Libeň
    Prague-Libeň is a district of Prague, Czech Republic, historically notable as the site of the World War II Operation Anthropoid assassination of Reinhard Heydrich.
  • E. Prague 8
    Prague 8 is a municipal district of Prague that includes a mix of historic neighborhoods and modern residential and commercial areas along the northeastern part of the city.
  • 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_69d6ab4964708190850585628b287b0c completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d915124e4c8190b0264c2a09e3c2f3 completed April 10, 2026, 3:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69f61e42cd588190835b3e8160bdbba5 completed May 2, 2026, 3:54 p.m.
Created at: April 8, 2026, 9:48 p.m.