Triple

T244820
Position Surface form Disambiguated ID Type / Status
Subject Jennifer Siebel Newsom E5013 entity
Predicate givenName P17 FINISHED
Object Jennifer
Jennifer is a common feminine given name of English origin, derived from the Cornish form of Guinevere and widely used in many English-speaking countries.
 E47548 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: Jennifer | Statement: [Jennifer Siebel Newsom, givenName, Jennifer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Jennifer
Context triple: [Jennifer Siebel Newsom, givenName, Jennifer]
  • A. Jane
    Jane is a feminine given name of English origin that has been widely used in many English-speaking countries for centuries.
  • B. Kathleen
    Kathleen is a feminine given name of Irish origin, derived from the name Catherine and widely used in English-speaking countries.
  • C. Kimberly
    Kimberly is a feminine given name of English origin that has been widely used in the United States since the mid-20th century.
  • D. Karen
    Karen is a common feminine given name used in many English-speaking and European countries.
  • E. Emma
    Emma is a common feminine given name of Germanic origin, widely used in English-speaking and many other countries.
  • 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: Jennifer
Triple: [Jennifer Siebel Newsom, givenName, Jennifer]
Generated description
Jennifer is a common feminine given name of English origin, derived from the Cornish form of Guinevere and widely used in many English-speaking countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Jennifer
Target entity description: Jennifer is a common feminine given name of English origin, derived from the Cornish form of Guinevere and widely used in many English-speaking countries.
  • A. Jane
    Jane is a feminine given name of English origin that has been widely used in many English-speaking countries for centuries.
  • B. Kathleen
    Kathleen is a feminine given name of Irish origin, derived from the name Catherine and widely used in English-speaking countries.
  • C. Kimberly
    Kimberly is a feminine given name of English origin that has been widely used in the United States since the mid-20th century.
  • D. Karen
    Karen is a common feminine given name used in many English-speaking and European countries.
  • E. Emma
    Emma is a common feminine given name of Germanic origin, widely used in English-speaking and many other countries.
  • 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_69a257c3d0708190b0871c4269d273e6 completed Feb. 28, 2026, 2:49 a.m.
NER Named-entity recognition batch_69a25d10ac248190a98dedabf5358668 completed Feb. 28, 2026, 3:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69a3f4d33cd48190b23d84be9cde773d completed March 1, 2026, 8:12 a.m.
NEDg Description generation batch_69a3f5841618819099adcf97cc794c69 completed March 1, 2026, 8:15 a.m.
NED2 Entity disambiguation (via description) batch_69a3f5cac5a08190a13ae5ffac770b1d completed March 1, 2026, 8:16 a.m.
Created at: Feb. 28, 2026, 2:53 a.m.