Triple

T14866692
Position Surface form Disambiguated ID Type / Status
Subject Városliget E349632 entity
Predicate hasEnglishName P3437 FINISHED
Object City Park
City Park is a large public park in Budapest, Hungary, known for its historic monuments, cultural institutions, and recreational spaces.
E1085376 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: City Park | Statement: [Városliget, hasEnglishName, City Park]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City Park
Context triple: [Városliget, hasEnglishName, City Park]
  • A. City Park
    City Park was the original name of Balboa Park, a large historic urban cultural park in San Diego, California.
  • B. City Park
    City Park is a historic public park in Launceston, Tasmania, known for its landscaped gardens, conservatory, and family-friendly recreational spaces.
  • C. City Park
    City Park is a public recreational park in Philomath, Oregon, offering green space and outdoor amenities for local residents and visitors.
  • D. City Park
    City Park is a public recreational park located in the city of Benicia, California.
  • E. City Park
    City Park is a central public green space and community gathering area located in Vergennes, Vermont.
  • 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: City Park
Triple: [Városliget, hasEnglishName, City Park]
Generated description
City Park is a large public park in Budapest, Hungary, known for its historic monuments, cultural institutions, and recreational spaces.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: City Park
Target entity description: City Park is a large public park in Budapest, Hungary, known for its historic monuments, cultural institutions, and recreational spaces.
  • A. City Park chosen
    City Park is one of Budapest’s largest and oldest public parks, known for its cultural attractions, historic monuments, and recreational spaces.
  • B. City Park
    City Park is a large historic urban park in Denver known for its lakes, recreation areas, and proximity to major cultural institutions like the Denver Zoo and Denver Museum of Nature & Science.
  • C. City Park
    City Park is a popular historic public park in Fort Collins, Colorado, known for its large lake, open green spaces, and community recreation areas.
  • D. City Park
    City Park is a public recreational park in Burlington, North Carolina, known for its family-friendly amenities and outdoor activities.
  • E. City Park
    City Park is a public recreational park in Philomath, Oregon, offering green space and outdoor amenities for local residents and visitors.
  • F. None of above.

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_69d822ed7e1881909b90fca143ad7e34 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded5761c688190b4477cb081554b51 completed April 15, 2026, 12:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe8bc9db7c8190af08b26471d28e97 completed May 9, 2026, 1:20 a.m.
NEDg Description generation batch_69fe8dda21188190b9e82c70ef3a3ec0 completed May 9, 2026, 1:28 a.m.
NED2 Entity disambiguation (via description) batch_69fe91a86ae881908b492a4f255866b9 completed May 9, 2026, 1:45 a.m.
Created at: April 10, 2026, 1:55 a.m.