Triple

T8947563
Position Surface form Disambiguated ID Type / Status
Subject New Town, Prague E213259 entity
Predicate locatedInAdministrativeEntity P40 FINISHED
Object Prague 2 E393450 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 2 | Statement: [New Town, Prague, locatedInAdministrativeEntity, Prague 2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Prague 2
Context triple: [New Town, Prague, locatedInAdministrativeEntity, Prague 2]
  • A. Prague 2 chosen
    Prague 2 is a central district of Prague, Czech Republic, known for its historic neighborhoods, parks, and landmarks such as Vyšehrad.
  • B. Prague 3
    Prague 3 is a central district of Prague known for its historic neighborhoods, including Žižkov, and notable cultural and religious sites such as the New Jewish Cemetery.
  • 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-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 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.
  • 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_69ca839843408190a39069a029a89f15 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc66deb8ec819087a9c5eddd24c08a completed April 1, 2026, 12:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfc2022e30819089ba08f494a3a66f completed April 3, 2026, 1:34 p.m.
Created at: March 30, 2026, 6:59 p.m.