Triple

T6394398
Position Surface form Disambiguated ID Type / Status
Subject Streymoy E143905 entity
Predicate hasCity P316 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: [Streymoy, hasCity, Tórshavn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tórshavn
Context triple: [Streymoy, hasCity, 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_69c008db906c819096f3597d55d95432 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c06881313481908e9082ffe57f29b6 completed March 22, 2026, 10:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6cafb3d4c8190a00e66839c3eaf01 completed March 27, 2026, 6:22 p.m.
Created at: March 22, 2026, 4:35 p.m.