Triple

T3301030
Position Surface form Disambiguated ID Type / Status
Subject Risky Business E69331 entity
Predicate mainCharacter P1183 FINISHED
Object Lana
Lana is the seductive call girl who becomes the central love interest and catalyst for chaos in the 1983 film "Risky Business."
E346443 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: Lana | Statement: [Risky Business, mainCharacter, Lana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lana
Context triple: [Risky Business, mainCharacter, Lana]
  • A. Lorna
    Lorna is a feminine given name most notably borne by American actress and singer Lorna Luft, the daughter of Judy Garland.
  • B. Lana Morris
    Lana Morris was a British film and television actress known for her work in mid-20th-century comedies and dramas.
  • C. Alana
    Alana is a feminine given name commonly used in English-speaking countries and various cultures worldwide.
  • D. Lena
    Lena is an alternate given name of Lee Krasner, the influential American abstract expressionist painter and wife of Jackson Pollock.
  • E. Lena
    Lena is a common feminine given name used in many languages, often derived from longer names such as Magdalena or Helena.
  • 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: Lana
Triple: [Risky Business, mainCharacter, Lana]
Generated description
Lana is the seductive call girl who becomes the central love interest and catalyst for chaos in the 1983 film "Risky Business."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Lana
Target entity description: Lana is the seductive call girl who becomes the central love interest and catalyst for chaos in the 1983 film "Risky Business."
  • A. Lorna
    Lorna is a feminine given name most notably borne by American actress and singer Lorna Luft, the daughter of Judy Garland.
  • B. Lana Morris
    Lana Morris was a British film and television actress known for her work in mid-20th-century comedies and dramas.
  • C. Alana
    Alana is a feminine given name commonly used in English-speaking countries and various cultures worldwide.
  • D. Lena
    Lena is an alternate given name of Lee Krasner, the influential American abstract expressionist painter and wife of Jackson Pollock.
  • E. Lena
    Lena is a common feminine given name used in many languages, often derived from longer names such as Magdalena or Helena.
  • 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_69ad859e529c8190a404273f53cb487d completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb0a7d224819080d9a638e08bb8a8 completed March 8, 2026, 5:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2f3db14ac819083182f56c60b61b2 completed March 12, 2026, 5:11 p.m.
NEDg Description generation batch_69b2fa0ed27c8190a32c153b44b7b2dd completed March 12, 2026, 5:38 p.m.
NED2 Entity disambiguation (via description) batch_69b312b273b48190a949e61b87722084 completed March 12, 2026, 7:23 p.m.
Created at: March 8, 2026, 3:11 p.m.