Triple

T22210571
Position Surface form Disambiguated ID Type / Status
Subject Van Helsing E548932 entity
Predicate featuresCharacter P626 FINISHED
Object Carl
Carl is a supporting character in the Van Helsing universe, portrayed as an inventive and bookish friar who aids the monster hunter with gadgets, research, and comic relief.
E870938 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: Carl | Statement: [Van Helsing, featuresCharacter, Carl]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Carl
Context triple: [Van Helsing, featuresCharacter, Carl]
  • A. Carl
    Carl is the given name of Carl Sagan, the renowned American astronomer, science communicator, and author.
  • B. Carl
    Carl is the given name of the influential American microbiologist Carl Woese, known for defining the Archaea domain of life.
  • C. Carl
    Carl is the given name of Carl Bernstein, the American investigative journalist renowned for his reporting on the Watergate scandal.
  • D. Carl
    Carl is the given name of Carl Viggo Lange, a Norwegian physician and politician.
  • E. Carl
    Carl is the given name of Carl Levin, a long-serving U.S. senator from Michigan known for his work on defense and government oversight.
  • 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: Carl
Triple: [Van Helsing, featuresCharacter, Carl]
Generated description
Carl is a supporting character in the Van Helsing universe, portrayed as an inventive and bookish friar who aids the monster hunter with gadgets, research, and comic relief.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Carl
Target entity description: Carl is a supporting character in the Van Helsing universe, portrayed as an inventive and bookish friar who aids the monster hunter with gadgets, research, and comic relief.
  • A. Carl chosen
    Carl is a supporting character in the film "Van Helsing," depicted as a witty and inventive friar who aids the titular monster hunter.
  • B. Carl
    Carl is a character from the Starship Troopers universe, known as Johnny Rico’s close friend who becomes a high-ranking intelligence officer.
  • C. Carl
    Carl is a central character in the fantasy-comedy television series "Special Unit 2," which follows a secret police unit that hunts mythological creatures in Chicago.
  • D. Carl
    Carl is a fictional character from the animated television series "The Adventures of Jimmy Neutron: Boy Genius," known for his timid personality and distinctive high-pitched voice.
  • E. Carl
    Carl is a fictional character from the U.S. television series "Shameless," known as the troublemaking youngest son of the Gallagher family.
  • 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_69e11e3f7e04819089806d81d5ac431e completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f12b2bcf748190a9721f0c9ae17e70 completed April 28, 2026, 9:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0aa61cc9e48190ba5f6eb2a7cf3c9b completed May 18, 2026, 5:39 a.m.
NEDg Description generation batch_6a0aa6c001d48190b49866f02514aa28 completed May 18, 2026, 5:42 a.m.
NED2 Entity disambiguation (via description) batch_6a0aa74204a081909fa0d80858bfa6bb completed May 18, 2026, 5:44 a.m.
Created at: April 16, 2026, 8:36 p.m.