Triple

T4045454
Position Surface form Disambiguated ID Type / Status
Subject Berg en Dal E84054 entity
Predicate contains P35 FINISHED
Object Ubbergen
Ubbergen is a village in the Dutch province of Gelderland, known for its scenic, hilly landscape near Nijmegen and the German border.
E408452 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: Ubbergen | Statement: [Berg en Dal, contains, Ubbergen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ubbergen
Context triple: [Berg en Dal, contains, Ubbergen]
  • A. Eemshaven
    Eemshaven is a major seaport and energy hub in the north of the Netherlands, known for its power plants, data centers, and offshore wind connections.
  • B. Wessum
    Wessum is a village and district within the town of Ahaus in the state of North Rhine-Westphalia, Germany.
  • C. Boxmeer
    Boxmeer is a town in the Dutch province of North Brabant, known historically as a former municipality and now part of the Land van Cuijk region.
  • D. Woudenberg
    Woudenberg is a small Dutch municipality and town located in the central Netherlands.
  • E. Wassenaar
    Wassenaar is an affluent coastal town in the western Netherlands known for its wooded estates, beaches, and role as a residential area for diplomats and expatriates.
  • 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: Ubbergen
Triple: [Berg en Dal, contains, Ubbergen]
Generated description
Ubbergen is a village in the Dutch province of Gelderland, known for its scenic, hilly landscape near Nijmegen and the German border.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Ubbergen
Target entity description: Ubbergen is a village in the Dutch province of Gelderland, known for its scenic, hilly landscape near Nijmegen and the German border.
  • A. Eemshaven
    Eemshaven is a major seaport and energy hub in the north of the Netherlands, known for its power plants, data centers, and offshore wind connections.
  • B. Wessum
    Wessum is a village and district within the town of Ahaus in the state of North Rhine-Westphalia, Germany.
  • C. Boxmeer
    Boxmeer is a town in the Dutch province of North Brabant, known historically as a former municipality and now part of the Land van Cuijk region.
  • D. Woudenberg
    Woudenberg is a small Dutch municipality and town located in the central Netherlands.
  • E. Wassenaar
    Wassenaar is an affluent coastal town in the western Netherlands known for its wooded estates, beaches, and role as a residential area for diplomats and expatriates.
  • 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_69aed930bd5c819083e7dcc14fc44f69 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefb5f85d48190ba80a0a24fbe438a completed March 9, 2026, 4:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b55652228c8190a9f301676deb0055 completed March 14, 2026, 12:36 p.m.
NEDg Description generation batch_69b556fec4708190b221893ec35f1a38 completed March 14, 2026, 12:39 p.m.
NED2 Entity disambiguation (via description) batch_69b557f73cbc8190b904089ab0fa97d6 completed March 14, 2026, 12:43 p.m.
Created at: March 9, 2026, 3:37 p.m.