Triple

T8978019
Position Surface form Disambiguated ID Type / Status
Subject Aloise Steiner Buckley E214445 entity
Predicate givenName P17 FINISHED
Object Aloise
Aloise is a given name, most notably borne by Aloise Steiner Buckley.
 E771532 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: Aloise | Statement: [Aloise Steiner Buckley, givenName, Aloise]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Aloise
Context triple: [Aloise Steiner Buckley, givenName, Aloise]
  • A. Aline
    Aline is a feminine given name of French origin, commonly used in various cultures and languages.
  • B. Arlette
    Arlette, also known as Herleva of Falaise, was the mother of William the Conqueror and a key figure in the early life of the first Norman king of England.
  • C. Madeleine
    Madeleine is a feminine given name, commonly used in French and English, derived from Magdalene and often associated with literary and cultural figures.
  • D. Madeleine
    Madeleine is a Paris Métro station in central Paris that serves as an interchange between several metro lines, including the automated Line 14.
  • E. Chantal
    Chantal is a commune located in the Sud Department of Haiti.
  • 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: Aloise
Triple: [Aloise Steiner Buckley, givenName, Aloise]
Generated description
Aloise is a given name, most notably borne by Aloise Steiner Buckley.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Aloise
Target entity description: Aloise is a given name, most notably borne by Aloise Steiner Buckley.
  • A. Aline
    Aline is a feminine given name of French origin, commonly used in various cultures and languages.
  • B. Arlette
    Arlette, also known as Herleva of Falaise, was the mother of William the Conqueror and a key figure in the early life of the first Norman king of England.
  • C. Madeleine
    Madeleine is a feminine given name, commonly used in French and English, derived from Magdalene and often associated with literary and cultural figures.
  • D. Madeleine
    Madeleine is a Paris Métro station in central Paris that serves as an interchange between several metro lines, including the automated Line 14.
  • E. Chantal
    Chantal is a commune located in the Sud Department of Haiti.
  • 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_69ca839ea8b88190922c6a326ffcc0d3 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc67a33c8481909125acf4b7f0a919 completed April 1, 2026, 12:32 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfd0afe9688190bdd66198ab31c2c7 completed April 3, 2026, 2:37 p.m.
NEDg Description generation batch_69cfd14a28d48190b63561f9a537daeb completed April 3, 2026, 2:40 p.m.
NED2 Entity disambiguation (via description) batch_69cfd1fd6db08190921285c3cfd3ce91 completed April 3, 2026, 2:43 p.m.
Created at: March 30, 2026, 7:02 p.m.