Triple

T19649864
Position Surface form Disambiguated ID Type / Status
Subject Dracula Has Risen from the Grave E471780 entity
Predicate featuresCharacter P626 FINISHED
Object Paul
Paul is a central character in the 1968 British horror film "Dracula Has Risen from the Grave," who becomes entangled in the struggle against Count Dracula.
E1388865 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: [Dracula Has Risen from the Grave, featuresCharacter, Paul]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paul
Context triple: [Dracula Has Risen from the Grave, featuresCharacter, Paul]
  • A. Paul
    Paul is a character in the film "Her," known as Theodore Twombly’s supportive and easygoing close friend.
  • B. Paul
    Paul is a 2011 sci-fi comedy film about two British geeks who encounter a wisecracking alien during a road trip across the United States.
  • C. Paul
    Paul is a village and civil parish in Cornwall, England, known for its historic church and coastal setting near Penzance.
  • D. Paul
    Paul is a character in the crime drama film "Never Die Alone," which follows the violent, intertwined lives of drug dealers and those around them.
  • E. Paul
    Paul is a central character in the psychological horror film "It Comes at Night," portrayed as a protective family man struggling to safeguard his loved ones amid a mysterious, apocalyptic threat.
  • 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: [Dracula Has Risen from the Grave, featuresCharacter, Paul]
Generated description
Paul is a central character in the 1968 British horror film "Dracula Has Risen from the Grave," who becomes entangled in the struggle against Count Dracula.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Paul
Target entity description: Paul is a central character in the 1968 British horror film "Dracula Has Risen from the Grave," who becomes entangled in the struggle against Count Dracula.
  • A. Paul
    Paul is a central character in the psychological horror film "It Comes at Night," portrayed as a protective family man struggling to safeguard his loved ones amid a mysterious, apocalyptic threat.
  • B. Paul
    Paul is a character in the crime drama film "Never Die Alone," which follows the violent, intertwined lives of drug dealers and those around them.
  • C. Paul
    Paul is a character from the film "Nobody’s Business," contributing to the story’s exploration of personal and family relationships.
  • D. Paul
    Paul is a character in the film "Her," known as Theodore Twombly’s supportive and easygoing close friend.
  • E. Paul
    Paul is the middle-aged American widower portrayed by Marlon Brando in the controversial 1972 film "Last Tango in Paris."
  • 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_69d8e51395348190ac1416d46dfc6db0 completed April 10, 2026, 11:54 a.m.
NER Named-entity recognition batch_69e641278e9881909bdf8d440ef6eba4 completed April 20, 2026, 3:07 p.m.
NED1 Entity disambiguation (via context triple) batch_6a077eeced6881909429563193fd71d1 completed May 15, 2026, 8:15 p.m.
NEDg Description generation batch_6a0783867f148190aaca0d1522689e3c completed May 15, 2026, 8:35 p.m.
NED2 Entity disambiguation (via description) batch_6a0784a7dba88190857275f577b5cc7c completed May 15, 2026, 8:40 p.m.
Created at: April 10, 2026, 1:44 p.m.