Triple

T4642922
Position Surface form Disambiguated ID Type / Status
Subject The Kids Are All Right E101695 entity
Predicate character P662 FINISHED
Object Paul
Paul is a laid-back, charming sperm donor whose unexpected involvement with his biological children disrupts a lesbian couple’s family dynamic in the film "The Kids Are All Right."
 E458813 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: Paul | Statement: [The Kids Are All Right, character, Paul]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Paul
Context triple: [The Kids Are All Right, character, Paul]
  • A. Paul
    Paul is a masculine given name of Latin origin, widely used in many Western and Christian-influenced cultures.
  • B. Paul
    Paul is the middle-aged American widower portrayed by Marlon Brando in the controversial 1972 film "Last Tango in Paris."
  • C. Paulus
    Paulus was an influential Roman jurist whose legal writings significantly shaped later compilations of Roman law.
  • D. Apostle Paul
    Apostle Paul was an early Christian missionary and theologian whose letters form a significant portion of the New Testament and profoundly shaped Christian doctrine.
  • E. Theophilus
    Theophilus was a prominent 6th-century Byzantine jurist and legal scholar who helped draft and interpret Emperor Justinian I’s codification of Roman law.
  • 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: Paul
Triple: [The Kids Are All Right, character, Paul]
Generated description
Paul is a laid-back, charming sperm donor whose unexpected involvement with his biological children disrupts a lesbian couple’s family dynamic in the film "The Kids Are All Right."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Paul
Target entity description: Paul is a laid-back, charming sperm donor whose unexpected involvement with his biological children disrupts a lesbian couple’s family dynamic in the film "The Kids Are All Right."
  • A. Paul
    Paul is a masculine given name of Latin origin, widely used in many Western and Christian-influenced cultures.
  • B. Paul
    Paul is the middle-aged American widower portrayed by Marlon Brando in the controversial 1972 film "Last Tango in Paris."
  • C. Paulus
    Paulus was an influential Roman jurist whose legal writings significantly shaped later compilations of Roman law.
  • D. Apostle Paul
    Apostle Paul was an early Christian missionary and theologian whose letters form a significant portion of the New Testament and profoundly shaped Christian doctrine.
  • E. Theophilus
    Theophilus was a prominent 6th-century Byzantine jurist and legal scholar who helped draft and interpret Emperor Justinian I’s codification of Roman law.
  • 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_69bd43d3bc7c81908f81fcf380476b0f completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd5a93047c8190990c94fd5a57c867 completed March 20, 2026, 2:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdfad80f14819097e022c9d9da17eb completed March 21, 2026, 1:56 a.m.
NEDg Description generation batch_69bdfdd7d3b881909dd8b362802005d8 completed March 21, 2026, 2:09 a.m.
NED2 Entity disambiguation (via description) batch_69bdfe48ed088190ba1de18bba4e9977 completed March 21, 2026, 2:11 a.m.
Created at: March 20, 2026, 1:14 p.m.