Triple

T1404820
Position Surface form Disambiguated ID Type / Status
Subject Lake Balaton E31666 entity
Predicate locatedNear P294 FINISHED
Object Keszthely
Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
 E168264 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: Keszthely | Statement: [Lake Balaton, locatedNear, Keszthely]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Keszthely
Context triple: [Lake Balaton, locatedNear, Keszthely]
  • A. Komló
    Komló is a town in southern Hungary known historically for its coal mining and hop-growing industries.
  • B. Kecskemét
    Kecskemét is a city in central Hungary known for its Art Nouveau architecture, cultural institutions, and role as an administrative and economic center of the region.
  • C. Sopron
    Sopron is a historic city in western Hungary near the Austrian border, known for its well-preserved medieval old town and wine-making traditions.
  • D. Tatabánya
    Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
  • E. Kaposvár, Hungary
    Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
  • 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: Keszthely
Triple: [Lake Balaton, locatedNear, Keszthely]
Generated description
Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Keszthely
Target entity description: Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
  • A. Komló
    Komló is a town in southern Hungary known historically for its coal mining and hop-growing industries.
  • B. Kecskemét
    Kecskemét is a city in central Hungary known for its Art Nouveau architecture, cultural institutions, and role as an administrative and economic center of the region.
  • C. Sopron
    Sopron is a historic city in western Hungary near the Austrian border, known for its well-preserved medieval old town and wine-making traditions.
  • D. Tatabánya
    Tatabánya is an industrial city in northwestern Hungary known for its mining heritage and role as a regional economic center.
  • E. Kaposvár, Hungary
    Kaposvár is a city in southwestern Hungary that serves as the administrative and cultural center of Somogy County.
  • 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_69a49918e1f88190ba610f9dc8114578 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c3bb3a9c81909db2ad91defd87b6 completed March 1, 2026, 10:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad159920d48190ace6bc4d37af75eb completed March 8, 2026, 6:22 a.m.
NEDg Description generation batch_69ad165a88388190b43067a5a8c625bf completed March 8, 2026, 6:25 a.m.
NED2 Entity disambiguation (via description) batch_69ad16be44f88190ab4ea4c9638bd141 completed March 8, 2026, 6:27 a.m.
Created at: March 1, 2026, 7:59 p.m.