Triple

T10220903
Position Surface form Disambiguated ID Type / Status
Subject Oldambtmeer E242574 entity
Predicate locatedIn P40 FINISHED
Object Blauwestad
Blauwestad is a modern lakeside residential and recreational area in the Dutch province of Groningen, developed around the artificial lake Oldambtmeer.
 E850265 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: Blauwestad | Statement: [Oldambtmeer, locatedIn, Blauwestad]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Blauwestad
Context triple: [Oldambtmeer, locatedIn, Blauwestad]
  • A. Bredasdorp
    Bredasdorp is a small agricultural town in South Africa’s Western Cape, known as a gateway to the southern Overberg region and nearby coastal and nature attractions.
  • B. Winburg
    Winburg is a small historic town in South Africa’s Free State province, known as one of the country’s oldest Voortrekker settlements.
  • C. Graskop
    Graskop is a small tourist town in northeastern South Africa known as a gateway to the Panorama Route and nearby natural attractions like waterfalls and the Blyde River Canyon.
  • D. Ermelo
    Ermelo is a key agricultural and transport hub town located in South Africa’s Mpumalanga province.
  • E. Swellendam
    Swellendam is a historic town in South Africa’s Western Cape, known for its Cape Dutch architecture and location near the Langeberg Mountains.
  • 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: Blauwestad
Triple: [Oldambtmeer, locatedIn, Blauwestad]
Generated description
Blauwestad is a modern lakeside residential and recreational area in the Dutch province of Groningen, developed around the artificial lake Oldambtmeer.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Blauwestad
Target entity description: Blauwestad is a modern lakeside residential and recreational area in the Dutch province of Groningen, developed around the artificial lake Oldambtmeer.
  • A. Bredasdorp
    Bredasdorp is a small agricultural town in South Africa’s Western Cape, known as a gateway to the southern Overberg region and nearby coastal and nature attractions.
  • B. Winburg
    Winburg is a small historic town in South Africa’s Free State province, known as one of the country’s oldest Voortrekker settlements.
  • C. Graskop
    Graskop is a small tourist town in northeastern South Africa known as a gateway to the Panorama Route and nearby natural attractions like waterfalls and the Blyde River Canyon.
  • D. Ermelo
    Ermelo is a key agricultural and transport hub town located in South Africa’s Mpumalanga province.
  • E. Swellendam
    Swellendam is a historic town in South Africa’s Western Cape, known for its Cape Dutch architecture and location near the Langeberg Mountains.
  • 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_69d381ae26c48190985abd0e25ee5d04 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d3aa72b258819097d8d50a714e19dc completed April 6, 2026, 12:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69d6a82c98fc8190929b7b56f9a6e60d completed April 8, 2026, 7:10 p.m.
NEDg Description generation batch_69d6ab2481b081908f65806c31be807f completed April 8, 2026, 7:23 p.m.
NED2 Entity disambiguation (via description) batch_69d6ad9b943c8190bbaf201f43b7444b completed April 8, 2026, 7:33 p.m.
Created at: April 6, 2026, 11:09 a.m.