Triple

T13555543
Position Surface form Disambiguated ID Type / Status
Subject Lisa Roberts Gillan E323762 entity
Predicate givenName P17 FINISHED
Object Lisa
Lisa is a feminine given name commonly used in English-speaking countries, often as a short form of Elizabeth or Melissa.
 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 Roberts Gillan, givenName, Lisa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lisa
Context triple: [Lisa Roberts Gillan, givenName, 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 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 Roberts Gillan, givenName, Lisa]
Generated description
Lisa is a feminine given name commonly used in English-speaking countries, often as a short form of Elizabeth or Melissa.
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 countries, often as a short form of Elizabeth or Melissa.
  • 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_69d8076830b48190910a902bae5888e2 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbaff3063c8190bd20149b3f7df352 completed April 12, 2026, 2:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69f75da95b7c8190af4fae155f01d3af completed May 3, 2026, 2:37 p.m.
NEDg Description generation batch_69f75ffd5a088190967dae3262a22302 completed May 3, 2026, 2:47 p.m.
NED2 Entity disambiguation (via description) batch_69f76072de148190b8911df91caa87b5 completed May 3, 2026, 2:49 p.m.
Created at: April 9, 2026, 9:47 p.m.