Triple

T6556726
Position Surface form Disambiguated ID Type / Status
Subject Nólsoy E152466 entity
Predicate locatedNear P294 FINISHED
Object Tórshavn E356497 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: Tórshavn | Statement: [Nólsoy, locatedNear, Tórshavn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tórshavn
Context triple: [Nólsoy, locatedNear, Tórshavn]
  • A. Tórshavn chosen
    Tórshavn is the capital and largest city of the Faroe Islands, serving as the political, cultural, and economic center of the archipelago.
  • B. Mariehamn
    Mariehamn is the main town and administrative, cultural, and economic center of the autonomous Åland Islands in the Baltic Sea.
  • C. Faro
    Faro is a historic coastal city in southern Portugal that serves as the capital of the Algarve region and a major gateway for tourism.
  • D. Faaborg
    Faaborg is a historic coastal town on the island of Funen in southern Denmark, known for its well-preserved old town, harbor, and cultural attractions.
  • E. Hirtshals
    Hirtshals is a Danish coastal town in northern Jutland known for its busy fishing and ferry port on the Skagerrak and its role as a key transport hub between Denmark and Norway.
  • 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_69c688058d6881908c19b309cc55dbfa completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6ae1d28bc8190a2fa4b3e1e39863c completed March 27, 2026, 4:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6e4234c148190b19d36903cb67651 completed March 27, 2026, 8:10 p.m.
Created at: March 27, 2026, 1:52 p.m.