Triple

T571560
Position Surface form Disambiguated ID Type / Status
Subject Charles E13673 entity
Predicate hasFeminineForm P1613 FINISHED
Object Charlene
Charlene is a feminine given name derived from the male name Charles.
 E95375 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: Charlene | Statement: [Charles, hasFeminineForm, Charlene]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Charlene
Context triple: [Charles, hasFeminineForm, Charlene]
  • A. Lina Lamont
    Lina Lamont is a comically vain, shrill-voiced silent film star whose struggle to adapt to talking pictures drives much of the humor and conflict in the classic musical film "Singin' in the Rain."
  • B. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • C. Charley St. James
    Charley St. James is a fictional character from the American prime-time soap opera "Falcon Crest," which centers on the intrigues and power struggles surrounding a California wine dynasty.
  • D. Patricia
    Patricia is a feminine given name of Latin origin, commonly used in English-speaking countries.
  • E. Nance
    Nance is the middle name of John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
  • 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: Charlene
Triple: [Charles, hasFeminineForm, Charlene]
Generated description
Charlene is a feminine given name derived from the male name Charles.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Charlene
Target entity description: Charlene is a feminine given name derived from the male name Charles.
  • A. Lina Lamont
    Lina Lamont is a comically vain, shrill-voiced silent film star whose struggle to adapt to talking pictures drives much of the humor and conflict in the classic musical film "Singin' in the Rain."
  • B. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • C. Charley St. James
    Charley St. James is a fictional character from the American prime-time soap opera "Falcon Crest," which centers on the intrigues and power struggles surrounding a California wine dynasty.
  • D. Patricia
    Patricia is a feminine given name of Latin origin, commonly used in English-speaking countries.
  • E. Nance
    Nance is the middle name of John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
  • 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_69a4933fa4d88190a7949cc83c08c5c1 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49b483ac08190b3be152a7cf42011 completed March 1, 2026, 8:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69a68916d9f081909a14612144491fb7 completed March 3, 2026, 7:09 a.m.
NEDg Description generation batch_69a68a478c9081908d8ccb1ce058e931 completed March 3, 2026, 7:14 a.m.
NED2 Entity disambiguation (via description) batch_69a6d72d0d0c8190abac6b2d5668a812 completed March 3, 2026, 12:42 p.m.
Created at: March 1, 2026, 7:33 p.m.