Triple

T1204103
Position Surface form Disambiguated ID Type / Status
Subject Lake Velence E25848 entity
Predicate locatedNear P294 FINISHED
Object Velence
Velence is a Hungarian town and popular resort destination on the shores of Lake Velence, known for its beaches, thermal waters, and recreational tourism.
 E137984 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: Velence | Statement: [Lake Velence, locatedNear, Velence]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Velence
Context triple: [Lake Velence, locatedNear, Velence]
  • A. Donuzlav
    Donuzlav is a deep-water lagoon and naval harbor in western Crimea that serves as a strategic base for Russian Black Sea naval operations.
  • B. Shipki La
    Shipki La is a high-altitude mountain pass on the India–China (Tibet) border in the Himalayas, serving as an important trade and transit route between the two countries.
  • C. Savski Venac
    Savski Venac is a central urban municipality of Belgrade, Serbia, known for its government institutions, major transport hubs, and historic neighborhoods.
  • D. Czarna Góra
    Czarna Góra is a village in southern Poland, known as a mountain resort area in the Tatra region.
  • E. Baturité
    Baturité is a municipality in the state of Ceará in northeastern Brazil, known for its mountainous terrain and relatively mild climate.
  • 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: Velence
Triple: [Lake Velence, locatedNear, Velence]
Generated description
Velence is a Hungarian town and popular resort destination on the shores of Lake Velence, known for its beaches, thermal waters, and recreational tourism.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Velence
Target entity description: Velence is a Hungarian town and popular resort destination on the shores of Lake Velence, known for its beaches, thermal waters, and recreational tourism.
  • A. Donuzlav
    Donuzlav is a deep-water lagoon and naval harbor in western Crimea that serves as a strategic base for Russian Black Sea naval operations.
  • B. Shipki La
    Shipki La is a high-altitude mountain pass on the India–China (Tibet) border in the Himalayas, serving as an important trade and transit route between the two countries.
  • C. Savski Venac
    Savski Venac is a central urban municipality of Belgrade, Serbia, known for its government institutions, major transport hubs, and historic neighborhoods.
  • D. Czarna Góra
    Czarna Góra is a village in southern Poland, known as a mountain resort area in the Tatra region.
  • E. Baturité
    Baturité is a municipality in the state of Ceará in northeastern Brazil, known for its mountainous terrain and relatively mild climate.
  • 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_69a4942b30f08190a91c60573e16b5ef completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bdbf94188190991f63a84cc76b8a completed March 1, 2026, 10:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac7f3caed481908e9e4b6b9daa544d completed March 7, 2026, 7:40 p.m.
NEDg Description generation batch_69ac7fa90f608190bec0c7dff1d7c9ae completed March 7, 2026, 7:42 p.m.
NED2 Entity disambiguation (via description) batch_69ac808a919081908253d1778695ab2d completed March 7, 2026, 7:46 p.m.
Created at: March 1, 2026, 7:46 p.m.