Triple

T1204106
Position Surface form Disambiguated ID Type / Status
Subject Lake Velence E25848 entity
Predicate locatedNear P294 FINISHED
Object Sukoró
Sukoró is a village in Hungary’s Fejér County, known as a lakeside resort and recreational area on the northern shore of Lake Velence.
 E141255 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: Sukoró | Statement: [Lake Velence, locatedNear, Sukoró]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sukoró
Context triple: [Lake Velence, locatedNear, Sukoró]
  • A. Sasak
    Sasak is an Austronesian language spoken primarily by the Sasak people on the Indonesian island of Lombok.
  • B. Manggala
    Manggala was a Mongol prince of the 13th century, notable as one of the sons of the Yuan dynasty founder Kublai Khan.
  • C. Kunama
    Kunama is a Nilo-Saharan language spoken primarily by the Kunama people in western Eritrea and adjacent parts of Ethiopia.
  • D. Ranu Kumbolo
    Ranu Kumbolo is a scenic high-altitude lake in East Java, Indonesia, popular as a rest and camping spot for hikers on the route to Mount Semeru.
  • E. Watugaluh
    Watugaluh was an important historical city in Java that served as the political and administrative center of the Medang Kingdom.
  • 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: Sukoró
Triple: [Lake Velence, locatedNear, Sukoró]
Generated description
Sukoró is a village in Hungary’s Fejér County, known as a lakeside resort and recreational area on the northern shore of Lake Velence.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sukoró
Target entity description: Sukoró is a village in Hungary’s Fejér County, known as a lakeside resort and recreational area on the northern shore of Lake Velence.
  • A. Sasak
    Sasak is an Austronesian language spoken primarily by the Sasak people on the Indonesian island of Lombok.
  • B. Manggala
    Manggala was a Mongol prince of the 13th century, notable as one of the sons of the Yuan dynasty founder Kublai Khan.
  • C. Kunama
    Kunama is a Nilo-Saharan language spoken primarily by the Kunama people in western Eritrea and adjacent parts of Ethiopia.
  • D. Ranu Kumbolo
    Ranu Kumbolo is a scenic high-altitude lake in East Java, Indonesia, popular as a rest and camping spot for hikers on the route to Mount Semeru.
  • E. Watugaluh
    Watugaluh was an important historical city in Java that served as the political and administrative center of the Medang Kingdom.
  • 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_69ac8a08e1a881908b3f3a41cc1fb010 completed March 7, 2026, 8:26 p.m.
NEDg Description generation batch_69ac8a8a9b208190aed3bcc4697415d1 completed March 7, 2026, 8:28 p.m.
NED2 Entity disambiguation (via description) batch_69ac8b17ae048190b32e89d2f2a60ba3 completed March 7, 2026, 8:31 p.m.
Created at: March 1, 2026, 7:46 p.m.