Triple

T665284
Position Surface form Disambiguated ID Type / Status
Subject Jan van Riebeeck E12847 entity
Predicate givenName P17 FINISHED
Object Johan
Johan is a given name associated with historical figures such as Jan van Riebeeck, the Dutch colonial administrator who founded Cape Town.
 E27352 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: Johan | Statement: [Jan van Riebeeck, givenName, Johan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Johan
Context triple: [Jan van Riebeeck, givenName, Johan]
  • A. Johan
    Johan is the given first name of J. Erik Jonsson, an American businessman and philanthropist who co-founded Texas Instruments and served as mayor of Dallas.
  • B. Johanus
    Johanus is a given name, likely a variant or diminutive of Johan, used as a personal first name in some cultures.
  • C. Johann
    Johann is a given name of Germanic origin commonly used in German-speaking and other European countries.
  • D. Andreas
    Andreas is a masculine given name of Greek origin, commonly used in various European and international cultures.
  • E. Johannes
    Johannes is the given first name of Paul Kruger, the prominent 19th-century Boer leader and president of the South African Republic.
  • 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: Johan
Triple: [Jan van Riebeeck, givenName, Johan]
Generated description
Johan is a given name associated with historical figures such as Jan van Riebeeck, the Dutch colonial administrator who founded Cape Town.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Johan
Target entity description: Johan is a given name associated with historical figures such as Jan van Riebeeck, the Dutch colonial administrator who founded Cape Town.
  • A. Johan
    Johan is the given first name of J. Erik Jonsson, an American businessman and philanthropist who co-founded Texas Instruments and served as mayor of Dallas.
  • B. Johanus
    Johanus is a given name, likely a variant or diminutive of Johan, used as a personal first name in some cultures.
  • C. Johann chosen
    Johann is a given name of Germanic origin commonly used in German-speaking and other European countries.
  • D. Andreas
    Andreas is a masculine given name of Greek origin, commonly used in various European and international cultures.
  • E. Johannes
    Johannes is the given first name of Paul Kruger, the prominent 19th-century Boer leader and president of the South African Republic.
  • F. None of above.

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_69a493355dec819098d4244b2fa34885 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49fd3d8fc8190866af5c76c08f486 completed March 1, 2026, 8:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69a6787173e08190bef6734294b60c13 completed March 3, 2026, 5:58 a.m.
NEDg Description generation batch_69a678dea88081908da0e9cbea83eadd completed March 3, 2026, 5:59 a.m.
NED2 Entity disambiguation (via description) batch_69a6792c41608190867fb257ff8d5d35 completed March 3, 2026, 6:01 a.m.
Created at: March 1, 2026, 7:36 p.m.