Triple

T1980513
Position Surface form Disambiguated ID Type / Status
Subject Annabella E43014 entity
Predicate givenName P17 FINISHED
Object Suzanne
Suzanne is a feminine given name of French origin, commonly used in various European and English-speaking countries.
 E151406 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: Suzanne | Statement: [Annabella, givenName, Suzanne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Suzanne
Context triple: [Annabella, givenName, Suzanne]
  • A. Suzanne
    "Suzanne" is a renowned song by Leonard Cohen, celebrated for its poetic lyrics and haunting melody.
  • B. Susanna
    Susanna is a deuterocanonical addition to the Book of Daniel, telling the story of a virtuous woman falsely accused of adultery and vindicated by the prophet Daniel.
  • C. Louise
    Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
  • D. Felicia
    Felicia is a feminine given name of Latin origin meaning "happy" or "fortunate," used in various cultures around the world.
  • E. Samantha
    Samantha is the middle name of the fictional socialite Tracy Samantha Lord from the classic film and play "The Philadelphia Story."
  • 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: Suzanne
Triple: [Annabella, givenName, Suzanne]
Generated description
Suzanne is a feminine given name of French origin, commonly used in various European and English-speaking countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Suzanne
Target entity description: Suzanne is a feminine given name of French origin, commonly used in various European and English-speaking countries.
  • A. Suzanne chosen
    "Suzanne" is a renowned song by Leonard Cohen, celebrated for its poetic lyrics and haunting melody.
  • B. Susanna
    Susanna is a deuterocanonical addition to the Book of Daniel, telling the story of a virtuous woman falsely accused of adultery and vindicated by the prophet Daniel.
  • C. Louise
    Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
  • D. Felicia
    Felicia is a feminine given name of Latin origin meaning "happy" or "fortunate," used in various cultures around the world.
  • E. Samantha
    Samantha is the middle name of the fictional socialite Tracy Samantha Lord from the classic film and play "The Philadelphia Story."
  • F. None of above.

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_69a88713ddc88190a969715658ebe7a8 completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb7c87bc081908ed179d1ca94fa3b completed March 7, 2026, 5:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69aeb3abc8cc819086e7b640d5231641 completed March 9, 2026, 11:48 a.m.
NEDg Description generation batch_69aeb5cf422c8190938b1c113270db58 completed March 9, 2026, 11:58 a.m.
NED2 Entity disambiguation (via description) batch_69aeb6327ad08190926eb12ffe8f317c completed March 9, 2026, 11:59 a.m.
Created at: March 4, 2026, 7:37 p.m.