Triple

T1512860
Position Surface form Disambiguated ID Type / Status
Subject Gulf of Bothnia E32052 entity
Predicate hasPort P35 FINISHED
Object Umeå
Umeå is a university city in northern Sweden known for its cultural scene, research institutions, and role as a regional economic hub.
 E232990 NE FINISHED

How this triple was built (4 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: Umeå | Statement: [Gulf of Bothnia, hasPort, Umeå]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Umeå
Context triple: [Gulf of Bothnia, hasPort, Umeå]
  • A. Luleå
    Luleå is a coastal city in northern Sweden known for its major port, technology and university hub, and proximity to the Arctic Circle.
  • B. Uppsala
    Uppsala is a historic Swedish city north of Stockholm, known for its prestigious university, medieval cathedral, and role as a cultural and ecclesiastical center.
  • C. Östersund
    Östersund is a city in central Sweden known for its strong winter sports tradition and repeated bids to host the Winter Olympics.
  • D. Trollhättan
    Trollhättan is a city in western Sweden known for its historic role in the automotive industry and as the longtime home of Saab Automobile’s main production facilities.
  • E. Nyköping
    Nyköping is a historic coastal town in southeastern Sweden known for its medieval castle, harbor, and role as a regional administrative and cultural center.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Umeå
Triple: [Gulf of Bothnia, hasPort, Umeå]
Generated description
Umeå is a university city in northern Sweden known for its cultural scene, research institutions, and role as a regional economic hub.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Umeå
Target entity description: Umeå is a university city in northern Sweden known for its cultural scene, research institutions, and role as a regional economic hub.
  • A. Luleå
    Luleå is a coastal city in northern Sweden known for its major port, technology and university hub, and proximity to the Arctic Circle.
  • B. Uppsala
    Uppsala is a historic Swedish city north of Stockholm, known for its prestigious university, medieval cathedral, and role as a cultural and ecclesiastical center.
  • C. Östersund
    Östersund is a city in central Sweden known for its strong winter sports tradition and repeated bids to host the Winter Olympics.
  • D. Trollhättan
    Trollhättan is a city in western Sweden known for its historic role in the automotive industry and as the longtime home of Saab Automobile’s main production facilities.
  • E. Nyköping
    Nyköping is a historic coastal town in southeastern Sweden known for its medieval castle, harbor, and role as a regional administrative and cultural center.
  • F. None of above. chosen

Provenance (5 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_69a885e8caf88190a5fbb6159ce87786 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a907d7cbf48190be40590a7f9fa1de completed March 5, 2026, 4:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae303a4f5881909746b1dae558f8b0 completed March 9, 2026, 2:28 a.m.
NEDg Description generation batch_69ae30e0411c81908cd19bf8d3525056 completed March 9, 2026, 2:30 a.m.
NED2 Entity disambiguation (via description) batch_69ae31849d7c8190a4e3c90334bf21a5 completed March 9, 2026, 2:33 a.m.
Created at: March 4, 2026, 7:26 p.m.