Triple

T7303013
Position Surface form Disambiguated ID Type / Status
Subject Karas Region E167904 entity
Predicate containsTown P847 FINISHED
Object Karasburg
Karasburg is a small town in southern Namibia that serves as a local commercial and transport hub near the South African border.
 E654905 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: Karasburg | Statement: [Karas Region, containsTown, Karasburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Karasburg
Context triple: [Karas Region, containsTown, Karasburg]
  • A. Labpur
    Labpur is a town and administrative center in the Birbhum district of West Bengal, India, known for its rural cultural heritage and local markets.
  • B. Lydenburg
    Lydenburg is a historic town in South Africa known for its early gold-mining heritage and proximity to scenic routes and nature reserves in the Mpumalanga province.
  • C. Koutiala
    Koutiala is a major city in southern Mali known as an important center for cotton production and agriculture.
  • D. Temeswar
    Temeswar (Timișoara) is a major city in western Romania known for its multicultural heritage and role in the 1989 Romanian Revolution.
  • E. Lüderitz
    Lüderitz is a coastal town in southwestern Namibia known for its Atlantic shoreline, nearby desert landscapes, and rich marine ecosystem influenced by the Benguela Current.
  • 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: Karasburg
Triple: [Karas Region, containsTown, Karasburg]
Generated description
Karasburg is a small town in southern Namibia that serves as a local commercial and transport hub near the South African border.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Karasburg
Target entity description: Karasburg is a small town in southern Namibia that serves as a local commercial and transport hub near the South African border.
  • A. Labpur
    Labpur is a town and administrative center in the Birbhum district of West Bengal, India, known for its rural cultural heritage and local markets.
  • B. Lydenburg
    Lydenburg is a historic town in South Africa known for its early gold-mining heritage and proximity to scenic routes and nature reserves in the Mpumalanga province.
  • C. Koutiala
    Koutiala is a major city in southern Mali known as an important center for cotton production and agriculture.
  • D. Temeswar
    Temeswar (Timișoara) is a major city in western Romania known for its multicultural heritage and role in the 1989 Romanian Revolution.
  • E. Lüderitz
    Lüderitz is a coastal town in southwestern Namibia known for its Atlantic shoreline, nearby desert landscapes, and rich marine ecosystem influenced by the Benguela Current.
  • 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_69c6888c820881909fc68f689fe1c251 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6ebb2261c8190ae9095c8e110b528 completed March 27, 2026, 8:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7e558098c819091562566c59332e2 completed March 28, 2026, 2:27 p.m.
NEDg Description generation batch_69c7e6671e2c8190aed42aa673540efa completed March 28, 2026, 2:32 p.m.
NED2 Entity disambiguation (via description) batch_69c7e6cd820881909ef8fd3bc28d2716 completed March 28, 2026, 2:33 p.m.
Created at: March 27, 2026, 3:01 p.m.