Triple

T22348070
Position Surface form Disambiguated ID Type / Status
Subject Jean Fetter E552448 entity
Predicate givenName P17 FINISHED
Object Jean
Jean is a given name used in various cultures, often as a form of John in English or the equivalent of John/Jean in French-speaking contexts.
E209182 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: Jean | Statement: [Jean Fetter, givenName, Jean]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jean
Context triple: [Jean Fetter, givenName, Jean]
  • A. Jean
    Jean is the given first name of Henry Dunant, the Swiss humanitarian who founded the Red Cross and received the first Nobel Peace Prize.
  • B. Jean
    Jean is a fictional mother character from the film "Sweet Sixteen."
  • C. Jean
    Jean is the central protagonist of the crime drama film "I'm Your Woman," a young mother forced into a perilous life on the run after her husband's criminal activities unravel.
  • D. Jean
    Jean is the given name of the actress better known professionally as Vivean Gray, recognized for her roles in British and Australian film and television.
  • E. Jean
    Jean is a fictional character associated with the story of the rhinoceros, likely from Eugène Ionesco’s absurdist play "Rhinocéros."
  • 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: Jean
Triple: [Jean Fetter, givenName, Jean]
Generated description
Jean is a given name used in various cultures, often as a form of John in English or the equivalent of John/Jean in French-speaking contexts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Jean
Target entity description: Jean is a given name used in various cultures, often as a form of John in English or the equivalent of John/Jean in French-speaking contexts.
  • A. Jean chosen
    Jean is a common French given name used for both males and females, equivalent to "John" in English.
  • B. Jean
    Jean is a person connected in some way to Johannes, likely as a relative, associate, or close acquaintance.
  • C. Jean
    Jean is the given name of the actress better known professionally as Vivean Gray, recognized for her roles in British and Australian film and television.
  • D. Jean
    Jean is the given first name of the Canadian novelist Margaret Laurence, a central figure in 20th-century Canadian literature.
  • E. Jean
    Jean is the given first name of Henry Dunant, the Swiss humanitarian who founded the Red Cross and received the first Nobel Peace Prize.
  • 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_69e11e4a0ad08190a385b4d343cf6524 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f157995bec819080b8d05fa88704ed completed April 29, 2026, 12:58 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0ad5106fc881908929055c7ce314e0 completed May 18, 2026, 9 a.m.
NEDg Description generation batch_6a0ad9947a8481909b36b72d15432d63 completed May 18, 2026, 9:19 a.m.
NED2 Entity disambiguation (via description) batch_6a0ada8fe7208190bc1143e328abc112 completed May 18, 2026, 9:23 a.m.
Created at: April 16, 2026, 8:43 p.m.