Triple

T9229751
Position Surface form Disambiguated ID Type / Status
Subject Koos de la Rey E221784 entity
Predicate residence P75 FINISHED
Object Lichtenburg
Lichtenburg is a town in South Africa’s North West Province, historically significant in the Second Anglo-Boer War and later known for its diamond discoveries and agricultural activity.
 E786931 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: Lichtenburg | Statement: [Koos de la Rey, residence, Lichtenburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lichtenburg
Context triple: [Koos de la Rey, residence, Lichtenburg]
  • A. Lilienthal
    Lilienthal is a German-origin surname borne by various notable individuals, including figures in aviation, science, and public service.
  • B. Luxenberg
    Luxenberg is an alternative spelling or variant form of the name "Luxemburg," which can refer to the European country Luxembourg or the surname of notable individuals such as revolutionary Rosa Luxemburg.
  • C. Lülsfeld
    Lülsfeld is a small municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
  • D. Landsberg
    Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
  • E. Seelingstädt
    Seelingstädt is a village and subdivision of the town of Trebsen in the German state of Saxony.
  • 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: Lichtenburg
Triple: [Koos de la Rey, residence, Lichtenburg]
Generated description
Lichtenburg is a town in South Africa’s North West Province, historically significant in the Second Anglo-Boer War and later known for its diamond discoveries and agricultural activity.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lichtenburg
Target entity description: Lichtenburg is a town in South Africa’s North West Province, historically significant in the Second Anglo-Boer War and later known for its diamond discoveries and agricultural activity.
  • A. Lilienthal
    Lilienthal is a German-origin surname borne by various notable individuals, including figures in aviation, science, and public service.
  • B. Luxenberg
    Luxenberg is an alternative spelling or variant form of the name "Luxemburg," which can refer to the European country Luxembourg or the surname of notable individuals such as revolutionary Rosa Luxemburg.
  • C. Lülsfeld
    Lülsfeld is a small municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
  • D. Landsberg
    Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
  • E. Seelingstädt
    Seelingstädt is a village and subdivision of the town of Trebsen in the German state of Saxony.
  • 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_69ca83ed628c8190bc02d641e57f097f completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccee17a2dc8190b373f78be7247f0d completed April 1, 2026, 10:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69d077a789f0819087d7f7612bbeaf6a completed April 4, 2026, 2:29 a.m.
NEDg Description generation batch_69d07a23205881909d96450759016e94 completed April 4, 2026, 2:40 a.m.
NED2 Entity disambiguation (via description) batch_69d07aaf06a08190b721e4b3f5a65c29 completed April 4, 2026, 2:42 a.m.
Created at: March 30, 2026, 7:29 p.m.