Triple

T7591437
Position Surface form Disambiguated ID Type / Status
Subject Fyn E179743 entity
Predicate hasSurroundingIslands P19485 FINISHED
Object Avernakø
Avernakø is a small Danish island in the South Funen Archipelago known for its rural landscapes, coastal scenery, and traditional village life.
 E675342 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: Avernakø | Statement: [Fyn, hasSurroundingIslands, Avernakø]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Avernakø
Context triple: [Fyn, hasSurroundingIslands, Avernakø]
  • A. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • B. Kvænangen
    Kvænangen is a fjord in northern Norway known for its dramatic coastal scenery, rich marine life, and traditional fishing communities.
  • C. Norg
    Norg is a village in the Dutch province of Drenthe, known for its historic farms, surrounding forests, and role as a local tourist destination.
  • D. Rennebu
    Rennebu is a rural municipality in Trøndelag county, Norway, known for its distinctive Y-shaped church and scenic valley landscapes.
  • E. Synnervika
    Synnervika is a small lakeside locality in Norway that serves as a key access point and harbor area on the shores of Lake Femunden.
  • 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: Avernakø
Triple: [Fyn, hasSurroundingIslands, Avernakø]
Generated description
Avernakø is a small Danish island in the South Funen Archipelago known for its rural landscapes, coastal scenery, and traditional village life.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Avernakø
Target entity description: Avernakø is a small Danish island in the South Funen Archipelago known for its rural landscapes, coastal scenery, and traditional village life.
  • A. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • B. Kvænangen
    Kvænangen is a fjord in northern Norway known for its dramatic coastal scenery, rich marine life, and traditional fishing communities.
  • C. Norg
    Norg is a village in the Dutch province of Drenthe, known for its historic farms, surrounding forests, and role as a local tourist destination.
  • D. Rennebu
    Rennebu is a rural municipality in Trøndelag county, Norway, known for its distinctive Y-shaped church and scenic valley landscapes.
  • E. Synnervika
    Synnervika is a small lakeside locality in Norway that serves as a key access point and harbor area on the shores of Lake Femunden.
  • 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_69c69f335248819093c1006f30513708 completed March 27, 2026, 3:16 p.m.
NER Named-entity recognition batch_69c701731a288190b53ffc546a2f47d7 completed March 27, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69c86192b5d88190b0a02cf303462bfb completed March 28, 2026, 11:17 p.m.
NEDg Description generation batch_69c8628d252c8190bc67e90f497f1ada completed March 28, 2026, 11:21 p.m.
NED2 Entity disambiguation (via description) batch_69c8631e5c2c8190b1c593ca9bbf039c completed March 28, 2026, 11:24 p.m.
Created at: March 27, 2026, 3:53 p.m.