Triple

T1151759
Position Surface form Disambiguated ID Type / Status
Subject Belshazzar E23691 entity
Predicate nameVariant P744 FINISHED
Object Baltasar
Baltasar is a variant of the name Belshazzar, historically associated with the last king of Babylon mentioned in the biblical Book of Daniel.
 E156875 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: Baltasar | Statement: [Belshazzar, nameVariant, Baltasar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Baltasar
Context triple: [Belshazzar, nameVariant, Baltasar]
  • A. Guillermo
    Guillermo is the Spanish form of the given name William, commonly used in Spanish-speaking countries.
  • B. Íñigo
    Íñigo is the Basque given name of Ignatius of Loyola, the 16th-century Spanish priest who founded the Society of Jesus (Jesuits).
  • C. Gonzalo
    Gonzalo is a masculine given name of Spanish origin, historically borne by notable figures such as conquistadors, nobles, and literary characters.
  • D. Eduardo
    Eduardo is a masculine given name commonly used in Spanish and Portuguese-speaking countries, equivalent to the English name Edward.
  • E. Azaña
    Azaña is the surname of Manuel Azaña, a prominent Spanish politician and writer who served as President of the Second Spanish 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: Baltasar
Triple: [Belshazzar, nameVariant, Baltasar]
Generated description
Baltasar is a variant of the name Belshazzar, historically associated with the last king of Babylon mentioned in the biblical Book of Daniel.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Baltasar
Target entity description: Baltasar is a variant of the name Belshazzar, historically associated with the last king of Babylon mentioned in the biblical Book of Daniel.
  • A. Guillermo
    Guillermo is the Spanish form of the given name William, commonly used in Spanish-speaking countries.
  • B. Íñigo
    Íñigo is the Basque given name of Ignatius of Loyola, the 16th-century Spanish priest who founded the Society of Jesus (Jesuits).
  • C. Gonzalo
    Gonzalo is a masculine given name of Spanish origin, historically borne by notable figures such as conquistadors, nobles, and literary characters.
  • D. Eduardo
    Eduardo is a masculine given name commonly used in Spanish and Portuguese-speaking countries, equivalent to the English name Edward.
  • E. Azaña
    Azaña is the surname of Manuel Azaña, a prominent Spanish politician and writer who served as President of the Second Spanish Republic.
  • 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_69a493f0d32c8190ac74bad3c87f2641 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4bc744e7c81908f8612f2aad28600 completed March 1, 2026, 10:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69acce57fbe081908a2060344c19141d completed March 8, 2026, 1:18 a.m.
NEDg Description generation batch_69acd06d000481909f6d934e857236f0 completed March 8, 2026, 1:27 a.m.
NED2 Entity disambiguation (via description) batch_69acd17b8c508190812b241d7906992b completed March 8, 2026, 1:31 a.m.
Created at: March 1, 2026, 7:44 p.m.