Triple

T16406834
Position Surface form Disambiguated ID Type / Status
Subject Dr. Lisa Cuddy E398454 entity
Predicate hasFirstName P17 FINISHED
Object Lisa
Lisa is the first name of Dr. Lisa Cuddy, a central hospital administrator character on the medical drama television series "House."
E1212639 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: [Dr. Lisa Cuddy, hasFirstName, Lisa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lisa
Context triple: [Dr. Lisa Cuddy, hasFirstName, 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 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.
  • 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: [Dr. Lisa Cuddy, hasFirstName, Lisa]
Generated description
Lisa is the first name of Dr. Lisa Cuddy, a central hospital administrator character on the medical drama television series "House."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Lisa
Target entity description: Lisa is the first name of Dr. Lisa Cuddy, a central hospital administrator character on the medical drama television series "House."
  • A. Lisa
    Lisa is the birth name of American actress and television personality Olivia Munn.
  • B. Lisa
    Lisa is the central protagonist of the French romantic drama film "L'Appartement," around whom the story’s mystery and emotional tension revolve.
  • C. Lisa
    Lisa is a person known primarily for holding a position or role that was later taken over by Denise.
  • D. Lisa
    Lisa is the central protagonist of the film "Wicker Park," around whom the story’s romantic mystery and emotional tension revolve.
  • E. 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.
  • 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_69d87f2950248190bc8ad9b9bebdc8c8 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e327d36b808190b6b8c412ae0d2faa completed April 18, 2026, 6:42 a.m.
NED1 Entity disambiguation (via context triple) batch_6a003c62614c8190acd6d211cab1be11 completed May 10, 2026, 8:05 a.m.
NEDg Description generation batch_6a003dc6f4888190ae326c9606d2a674 completed May 10, 2026, 8:11 a.m.
NED2 Entity disambiguation (via description) batch_6a0041842cc88190b81432d234f9aa17 completed May 10, 2026, 8:27 a.m.
Created at: April 10, 2026, 5:09 a.m.