Triple

T10676242
Position Surface form Disambiguated ID Type / Status
Subject Jaroměř E251624 entity
Predicate locatedOnRiver P165 FINISHED
Object Metuje E878636 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: Metuje | Statement: [Jaroměř, locatedOnRiver, Metuje]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Metuje
Context triple: [Jaroměř, locatedOnRiver, Metuje]
  • A. Metuje chosen
    Metuje is a river in northeastern Bohemia in the Czech Republic, known for flowing through towns such as Nové Město nad Metují and contributing to the Elbe river basin.
  • B. Mézin
    Mézin is a small commune in southwestern France, known as the birthplace of former French President Armand Fallières.
  • C. Uherka
    Uherka is a river in eastern Poland that serves as a tributary of the Western Bug, flowing through the Lublin region.
  • D. Muhos
    Muhos is a municipality in Northern Ostrobothnia, Finland, known for its riverside landscapes along the Oulujoki and its proximity to the city of Oulu.
  • E. Munirka
    Munirka is a densely populated residential and commercial neighborhood in South Delhi, known for its urban village character, proximity to major institutions, and extensive rental housing.
  • 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_69d6aa5b0d2881909584b20efc5877f0 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6fb94b05c8190b66bf64f5c6d166b completed April 9, 2026, 1:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69d998bfc27c8190a9d3e77fbe544a6d completed April 11, 2026, 12:41 a.m.
Created at: April 8, 2026, 9:09 p.m.