Triple

T3497399
Position Surface form Disambiguated ID Type / Status
Subject Anastasie de Lafayette E73883 entity
Predicate givenName P17 FINISHED
Object Anastasie
Anastasie is the given name of Anastasie de Lafayette, a French noblewoman associated with the influential Lafayette family.
 E362652 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: Anastasie | Statement: [Anastasie de Lafayette, givenName, Anastasie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Anastasie
Context triple: [Anastasie de Lafayette, givenName, Anastasie]
  • A. Alix
    Alix is the given name of Alix of Hesse and by Rhine, who became Empress Alexandra Feodorovna of Russia as the wife of Tsar Nicholas II.
  • B. Antoinette
    Antoinette is the birth name of Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
  • C. Antoinette
    Antoinette is a feminine given name of French origin, historically associated with nobility and later borne by various notable figures in the arts and public life.
  • D. Natalya
    Natalya is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and derived from the Latin name Natalia.
  • E. Natalia
    Natalia was a short-lived Boer republic established in the 1830s in what is now KwaZulu-Natal, South Africa.
  • 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: Anastasie
Triple: [Anastasie de Lafayette, givenName, Anastasie]
Generated description
Anastasie is the given name of Anastasie de Lafayette, a French noblewoman associated with the influential Lafayette family.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Anastasie
Target entity description: Anastasie is the given name of Anastasie de Lafayette, a French noblewoman associated with the influential Lafayette family.
  • A. Alix
    Alix is the given name of Alix of Hesse and by Rhine, who became Empress Alexandra Feodorovna of Russia as the wife of Tsar Nicholas II.
  • B. Antoinette
    Antoinette is the birth name of Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
  • C. Antoinette
    Antoinette is a feminine given name of French origin, historically associated with nobility and later borne by various notable figures in the arts and public life.
  • D. Natalya
    Natalya is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and derived from the Latin name Natalia.
  • E. Natalia
    Natalia was a short-lived Boer republic established in the 1830s in what is now KwaZulu-Natal, South Africa.
  • 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_69ad85cdb6e48190a335d412b9194ed8 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbbd299ec8190b76b165b2fd70537 completed March 8, 2026, 6:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69b373d011c0819088245afe03be3c44 completed March 13, 2026, 2:17 a.m.
NEDg Description generation batch_69b3745c7304819085a47af79cd738c0 completed March 13, 2026, 2:20 a.m.
NED2 Entity disambiguation (via description) batch_69b374f5999c8190ae48570a412dc6dc completed March 13, 2026, 2:22 a.m.
Created at: March 8, 2026, 3:18 p.m.