Triple

T14170313
Position Surface form Disambiguated ID Type / Status
Subject Lisa Howard E351187 entity
Predicate hasGivenName P17 FINISHED
Object Lisa
Lisa is a common feminine given name used in many English-speaking and European countries, often as a shortened form of Elizabeth.
E300630 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: Lisa | Statement: [Lisa Howard, hasGivenName, Lisa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lisa
Context triple: [Lisa Howard, hasGivenName, Lisa]
  • A. Lisa
    Lisa is a central character in the science fiction adventure film "Zathura: A Space Adventure," where she becomes unwittingly involved in her younger brothers' perilous journey through outer space.
  • B. Lisa
    Lisa is the central protagonist of the film "Wicker Park," around whom the story’s romantic mystery and emotional tension revolve.
  • C. Lisa
    Lisa is a custom-designed integrated circuit that served as a key support chipset component in early Apple Macintosh computers, handling functions such as memory and system control.
  • D. Lisa
    Lisa is a person known primarily for holding a position or role that was later taken over by Denise.
  • E. Lisa
    "Lisa" is a notable work by control theorist and Stanford professor Stephen Boyd, likely associated with his research in optimization and control systems.
  • 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: Lisa
Triple: [Lisa Howard, hasGivenName, Lisa]
Generated description
Lisa is a common feminine given name used in many English-speaking and European countries, often as a shortened form of Elizabeth.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Lisa
Target entity description: Lisa is a common feminine given name used in many English-speaking and European countries, often as a shortened form of Elizabeth.
  • A. Lisa chosen
    Lisa is a feminine given name commonly used in English-speaking countries, often as a shortened form of Elizabeth or Melissa.
  • B. Lisa
    Lisa is a person known primarily for holding a position or role that was later taken over by Denise.
  • C. Lisa
    Lisa is the given name of Australian musician and composer Lisa Gerrard, renowned for her work as part of Dead Can Dance and for her film scores.
  • D. Lisa
    Lisa is a fictional character known primarily as the romantic interest of Luke.
  • E. Lisa
    Lisa is the central female protagonist of the film "The Other Man," around whom the story’s romantic and dramatic tensions revolve.
  • 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_69d8278834a08190b0f1784e58d7b99c completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de61b472288190b4a271daa54aa6cd completed April 14, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcf7f779248190921c85f99f587296 completed May 7, 2026, 8:37 p.m.
NEDg Description generation batch_69fcf8bb58ac81908e66156a805edda8 completed May 7, 2026, 8:40 p.m.
NED2 Entity disambiguation (via description) batch_69fcf93b528c81908c0ee11908d25574 completed May 7, 2026, 8:42 p.m.
Created at: April 10, 2026, 1:01 a.m.