Triple

T4534733
Position Surface form Disambiguated ID Type / Status
Subject Zénaïde Bonaparte E107380 entity
Predicate givenName P17 FINISHED
Object Zénaïde
Zénaïde is a feminine given name of French origin, notably borne by Zénaïde Bonaparte, a member of Napoleon Bonaparte’s family.
 E450689 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: Zénaïde | Statement: [Zénaïde Bonaparte, givenName, Zénaïde]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Zénaïde
Context triple: [Zénaïde Bonaparte, givenName, Zénaïde]
  • A. Fernanda
    Fernanda is a feminine given name commonly used in Romance-language countries, derived from the masculine name Ferdinand.
  • B. María
    "María" is a film featuring actress Taryn Power in a significant role.
  • C. María
    María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
  • D. María
    María is the given first name of Josefa Ortiz de Domínguez, a prominent figure in Mexico’s War of Independence.
  • E. Rosaura
    Rosaura is a central character in Laura Esquivel’s novel "Like Water for Chocolate," known as Tita’s sister and romantic rival within the story’s intense family and culinary drama.
  • 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: Zénaïde
Triple: [Zénaïde Bonaparte, givenName, Zénaïde]
Generated description
Zénaïde is a feminine given name of French origin, notably borne by Zénaïde Bonaparte, a member of Napoleon Bonaparte’s family.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Zénaïde
Target entity description: Zénaïde is a feminine given name of French origin, notably borne by Zénaïde Bonaparte, a member of Napoleon Bonaparte’s family.
  • A. Fernanda
    Fernanda is a feminine given name commonly used in Romance-language countries, derived from the masculine name Ferdinand.
  • B. María
    "María" is a film featuring actress Taryn Power in a significant role.
  • C. María
    María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
  • D. María
    María is the given first name of Josefa Ortiz de Domínguez, a prominent figure in Mexico’s War of Independence.
  • E. Rosaura
    Rosaura is a central character in Laura Esquivel’s novel "Like Water for Chocolate," known as Tita’s sister and romantic rival within the story’s intense family and culinary drama.
  • 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_69bd43f922788190b7edfa294e39b178 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd57a2301c8190aa59280a16750156 completed March 20, 2026, 2:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdacf016d0819080665256c84d37a3 completed March 20, 2026, 8:24 p.m.
NEDg Description generation batch_69bdad81ace48190956f8f62610e188d completed March 20, 2026, 8:26 p.m.
NED2 Entity disambiguation (via description) batch_69bdadb912148190bf4390c31396f189 completed March 20, 2026, 8:27 p.m.
Created at: March 20, 2026, 1:04 p.m.