Triple

T7335658
Position Surface form Disambiguated ID Type / Status
Subject William I, Duke of Aquitaine E169119 entity
Predicate spouse P13 FINISHED
Object Adalinda
Adalinda was a medieval noblewoman known primarily as the wife of William I, Duke of Aquitaine.
 E657829 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: Adalinda | Statement: [William I, Duke of Aquitaine, spouse, Adalinda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Adalinda
Context triple: [William I, Duke of Aquitaine, spouse, Adalinda]
  • A. Luciana
    Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
  • B. Rosalinda
    Rosalinda is a feminine given name of Spanish and Italian origin, often interpreted to mean "beautiful rose."
  • C. Adelfia
    Adelfia is a town and comune in the Apulia region of southern Italy, known for its agricultural traditions and religious festivals.
  • D. Clorinda
    Clorinda is a border city in northeastern Argentina’s Formosa Province, located opposite Asunción, Paraguay, and serving as an important regional commercial and transport hub.
  • E. Rosabella
    Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
  • 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: Adalinda
Triple: [William I, Duke of Aquitaine, spouse, Adalinda]
Generated description
Adalinda was a medieval noblewoman known primarily as the wife of William I, Duke of Aquitaine.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Adalinda
Target entity description: Adalinda was a medieval noblewoman known primarily as the wife of William I, Duke of Aquitaine.
  • A. Luciana
    Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
  • B. Rosalinda
    Rosalinda is a feminine given name of Spanish and Italian origin, often interpreted to mean "beautiful rose."
  • C. Adelfia
    Adelfia is a town and comune in the Apulia region of southern Italy, known for its agricultural traditions and religious festivals.
  • D. Clorinda
    Clorinda is a border city in northeastern Argentina’s Formosa Province, located opposite Asunción, Paraguay, and serving as an important regional commercial and transport hub.
  • E. Rosabella
    Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
  • 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_69c68a568a6481908f11e20db7bc8446 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f0c38d6c81908a57ef1eea0e4951 completed March 27, 2026, 9:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7ef22d6ac819087f05d6e6787509a completed March 28, 2026, 3:09 p.m.
NEDg Description generation batch_69c7f58ddcf88190bfc15f673083c009 completed March 28, 2026, 3:36 p.m.
NED2 Entity disambiguation (via description) batch_69c7f615d6d48190933ac515874299b9 completed March 28, 2026, 3:39 p.m.
Created at: March 27, 2026, 3:04 p.m.