Triple

T8517635
Position Surface form Disambiguated ID Type / Status
Subject Nanette Lederer Calder E201613 entity
Predicate givenName P17 FINISHED
Object Nanette
Nanette is a feminine given name of French origin, commonly used in English- and French-speaking countries.
E739541 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: Nanette | Statement: [Nanette Lederer Calder, givenName, Nanette]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nanette
Context triple: [Nanette Lederer Calder, givenName, Nanette]
  • A. Bettina
    Bettina is a feminine given name of Hebrew origin, often considered a diminutive of Elisabeth or Benedetta and used in various European languages.
  • B. Arabella
    Arabella is a feminine given name of Latin origin, often associated with elegance and used in various English-speaking cultures.
  • C. Arabella
    Arabella is a romantic opera in three acts by Richard Strauss, first performed in 1933, known for its lush orchestration and exploration of love and social expectations in 19th-century Vienna.
  • D. Mathilda
    Mathilda is the middle name of Elivera Mathilda Carlson Doud, the wife of former U.S. President Dwight D. Eisenhower.
  • E. Madama
    Madama is a Palestinian village located in the Nablus Governorate in the northern West Bank.
  • 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: Nanette
Triple: [Nanette Lederer Calder, givenName, Nanette]
Generated description
Nanette is a feminine given name of French origin, commonly used in English- and French-speaking countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Nanette
Target entity description: Nanette is a feminine given name of French origin, commonly used in English- and French-speaking countries.
  • A. Bettina
    Bettina is a feminine given name of Hebrew origin, often considered a diminutive of Elisabeth or Benedetta and used in various European languages.
  • B. Arabella
    Arabella is a feminine given name of Latin origin, often associated with elegance and used in various English-speaking cultures.
  • C. Arabella
    Arabella is a romantic opera in three acts by Richard Strauss, first performed in 1933, known for its lush orchestration and exploration of love and social expectations in 19th-century Vienna.
  • D. Mathilda
    Mathilda is the middle name of Elivera Mathilda Carlson Doud, the wife of former U.S. President Dwight D. Eisenhower.
  • E. Madama
    Madama is a Palestinian village located in the Nablus Governorate in the northern West Bank.
  • 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_69ca8321bb44819081b74df0b710276d completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe626787c819087e72dd76b2d9310 completed March 31, 2026, 3:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce4e6c93d081909da2a748b0fa6fd3 completed April 2, 2026, 11:09 a.m.
NEDg Description generation batch_69ce4ffc30e08190b71e941d63d56015 completed April 2, 2026, 11:16 a.m.
NED2 Entity disambiguation (via description) batch_69ce54dc664081908ff63ec7f92834d7 completed April 2, 2026, 11:37 a.m.
Created at: March 30, 2026, 6:15 p.m.