Triple

T17713442
Position Surface form Disambiguated ID Type / Status
Subject Utrechtse Heuvelrug E441626 entity
Predicate formedByMergerOf P77 FINISHED
Object Maarn 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: Maarn | Statement: [Utrechtse Heuvelrug, formedByMergerOf, Maarn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maarn
Context triple: [Utrechtse Heuvelrug, formedByMergerOf, Maarn]
  • A. Maarn chosen
    Maarn is a village in the Dutch province of Utrecht, known for its location on the Utrechtse Heuvelrug ridge and its surrounding natural landscapes.
  • B. Marien
    Marien is a given name most notably borne by Marien Ngouabi, the former president of the Republic of the Congo.
  • C. Maasin
    Maasin is a coastal city in the Philippines that serves as the administrative, economic, and religious center of the province of Southern Leyte.
  • D. Merant
    Merant was a software company known for its development and configuration management tools, including ownership of the PVCS version control product.
  • E. Mure
    Mure is a Scottish surname historically associated with Lowland families and often considered a variant of or related to the name Muir.
  • 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_69d8b9ea20b48190ace88bb46b01e6a9 completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e4729cebd08190872be96a26d0f7ce completed April 19, 2026, 6:13 a.m.
Created at: April 10, 2026, 10:06 a.m.