Triple

T12277305
Position Surface form Disambiguated ID Type / Status
Subject Lisa Kudrow E292621 entity
Predicate givenName P17 FINISHED
Object Lisa
Lisa is a feminine given name commonly used in English-speaking and many other countries, often as a short 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 Kudrow, givenName, Lisa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lisa
Context triple: [Lisa Kudrow, givenName, Lisa]
  • A. Lisa
    Lisa is the central protagonist of the film "Wicker Park," around whom the story’s romantic mystery and emotional tension revolve.
  • B. Lisa
    Lisa is a fictional character from the psychological horror film "The Voices," known for her involvement with the disturbed protagonist and the film’s darkly comedic, violent events.
  • 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 Kudrow, givenName, Lisa]
Generated description
Lisa is a feminine given name commonly used in English-speaking and many other countries, often as a short form of Elizabeth.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Lisa
Target entity description: Lisa is a feminine given name commonly used in English-speaking and many other countries, often as a short 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 the central female protagonist of the film "The Other Man," around whom the story’s romantic and dramatic tensions revolve.
  • E. Lisa
    Lisa is the central protagonist of the film "Wicker Park," around whom the story’s romantic mystery and emotional tension 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_69d6ab6856488190b5d31178d5015f8e completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d91cf06cf08190ac8671dd9bbed03d completed April 10, 2026, 3:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69f61e6d72d081908c8697257df712f1 completed May 2, 2026, 3:55 p.m.
NEDg Description generation batch_69f620759f348190baa9af5b33d4e37f completed May 2, 2026, 4:04 p.m.
NED2 Entity disambiguation (via description) batch_69f624bf23948190b182e4c31564d210 completed May 2, 2026, 4:22 p.m.
Created at: April 8, 2026, 9:52 p.m.