Triple

T4372002
Position Surface form Disambiguated ID Type / Status
Subject Faaborg Museum E98917 entity
Predicate location P40 FINISHED
Object 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.
 E438008 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: Faaborg | Statement: [Faaborg Museum, location, Faaborg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Faaborg
Context triple: [Faaborg Museum, location, Faaborg]
  • A. Nyborg
    Nyborg is a historic coastal town and former royal seat in central Denmark, located on the island of Funen.
  • B. Svendborg
    Svendborg is a historic coastal town and seaport in southern Denmark known for its maritime heritage and location on the island of Funen.
  • C. Holstebro
    Holstebro is a town in western Jutland, Denmark, known as a regional center that hosts significant Danish Army military facilities.
  • D. 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.
  • E. Fredericia
    Fredericia is a Danish coastal town in Jutland known for its historic 17th-century fortress and well-preserved ramparts.
  • 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: Faaborg
Triple: [Faaborg Museum, location, Faaborg]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Faaborg
Target entity description: 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.
  • A. Nyborg
    Nyborg is a historic coastal town and former royal seat in central Denmark, located on the island of Funen.
  • B. Svendborg
    Svendborg is a historic coastal town and seaport in southern Denmark known for its maritime heritage and location on the island of Funen.
  • C. Holstebro
    Holstebro is a town in western Jutland, Denmark, known as a regional center that hosts significant Danish Army military facilities.
  • D. 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.
  • E. Fredericia
    Fredericia is a Danish coastal town in Jutland known for its historic 17th-century fortress and well-preserved ramparts.
  • 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_69b3454db3708190aeafd814413c4c3d completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3521dffbc8190b9300a7f4f64bdc0 completed March 12, 2026, 11:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5f5ddcbc08190baac9dbe041a66ad completed March 14, 2026, 11:57 p.m.
NEDg Description generation batch_69b5f7a24c148190b3f5c71f761518d4 completed March 15, 2026, 12:04 a.m.
NED2 Entity disambiguation (via description) batch_69b5f7fa2aa881908a23299ea82602c0 completed March 15, 2026, 12:06 a.m.
Created at: March 12, 2026, 11:17 p.m.