Triple

T2061006
Position Surface form Disambiguated ID Type / Status
Subject Lisa Kron E45788 entity
Predicate givenName 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.
 E77314 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 Kron, givenName, Lisa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lisa
Context triple: [Lisa Kron, 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. Laura
    Laura is a classic 1944 American film noir mystery celebrated for its sophisticated storytelling, atmospheric cinematography, and iconic score.
  • C. Laura
    Laura is a feminine given name of Latin origin, commonly used in many languages and cultures.
  • D. Jennifer
    Jennifer is a common feminine given name of English origin, derived from the Cornish form of Guinevere and widely used in many English-speaking countries.
  • E. Jane
    Jane is a feminine given name of English origin that has been widely used in many English-speaking countries for centuries.
  • 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 Kron, givenName, 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 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. Laura
    Laura is a classic 1944 American film noir mystery celebrated for its sophisticated storytelling, atmospheric cinematography, and iconic score.
  • C. Laura
    Laura is a feminine given name of Latin origin, commonly used in many languages and cultures.
  • D. Jennifer
    Jennifer is a common feminine given name of English origin, derived from the Cornish form of Guinevere and widely used in many English-speaking countries.
  • E. Jane
    Jane is a feminine given name of English origin that has been widely used in many English-speaking countries for centuries.
  • 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_69a8891a19508190a12ef1e192308dcb completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb9d0ecf08190aec20338a6ba9911 completed March 7, 2026, 5:38 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae2017998c8190976c2111f140b90a completed March 9, 2026, 1:19 a.m.
NEDg Description generation batch_69ae20b77cf081908ac4d94283202d51 completed March 9, 2026, 1:21 a.m.
NED2 Entity disambiguation (via description) batch_69ae213410708190af9715f488c5b8f5 completed March 9, 2026, 1:24 a.m.
Created at: March 4, 2026, 7:40 p.m.