Triple

T7799556
Position Surface form Disambiguated ID Type / Status
Subject Ridderkerk E180392 entity
Predicate hasSubdivision P747 FINISHED
Object Centrum
Centrum is the central district and main urban core of the Dutch municipality of Ridderkerk.
 E695529 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: Centrum | Statement: [Ridderkerk, hasSubdivision, Centrum]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Centrum
Context triple: [Ridderkerk, hasSubdivision, Centrum]
  • A. Centrum
    Centrum is the central urban district and main commercial area of the Dutch city of Meppel.
  • B. Innenstadt
    Innenstadt is the central urban district of Frankfurt am Main, known as the city’s historic core and primary commercial area.
  • C. Centrs
    Centrs is the central district of Riga, Latvia, known for its historic architecture, cultural institutions, and commercial activity.
  • D. Centrale
    Centrale is a major shopping centre located in the London Borough of Croydon, featuring a wide range of retail stores and services.
  • E. Centrale
    Centrale is a prestigious French engineering school renowned for its rigorous scientific curriculum and role in training elite engineers and industry leaders.
  • 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: Centrum
Triple: [Ridderkerk, hasSubdivision, Centrum]
Generated description
Centrum is the central district and main urban core of the Dutch municipality of Ridderkerk.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Centrum
Target entity description: Centrum is the central district and main urban core of the Dutch municipality of Ridderkerk.
  • A. Centrum
    Centrum is the central urban district and main commercial area of the Dutch city of Meppel.
  • B. Innenstadt
    Innenstadt is the central urban district of Frankfurt am Main, known as the city’s historic core and primary commercial area.
  • C. Centrs
    Centrs is the central district of Riga, Latvia, known for its historic architecture, cultural institutions, and commercial activity.
  • D. Centrale
    Centrale is a prestigious French engineering school renowned for its rigorous scientific curriculum and role in training elite engineers and industry leaders.
  • E. Centrale
    Centrale is a major shopping centre located in the London Borough of Croydon, featuring a wide range of retail stores and services.
  • 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_69ca827e50cc8190a92a733577184938 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cae985d8f08190b38d9d6848a7dc83 completed March 30, 2026, 9:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69cb142117b48190bdc17677592bfa8f completed March 31, 2026, 12:24 a.m.
NEDg Description generation batch_69cb1a1414a08190912945cee30e6bc2 completed March 31, 2026, 12:49 a.m.
NED2 Entity disambiguation (via description) batch_69cb3843368881908d7c4d5a81c53a23 completed March 31, 2026, 2:58 a.m.
Created at: March 30, 2026, 4:32 p.m.