Triple

T13933365
Position Surface form Disambiguated ID Type / Status
Subject The Continental E335046 entity
Predicate character P662 FINISHED
Object Lou
Lou is a character in the John Wick universe’s TV series "The Continental," involved in the gritty underworld surrounding the infamous assassin hotel.
 E1056863 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: Lou | Statement: [The Continental, character, Lou]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lou
Context triple: [The Continental, character, Lou]
  • A. Lou
    Lou is a character from the virtual reality co-op shooter game "After the Fall," set in a post-apocalyptic, frozen Los Angeles overrun by mutated creatures.
  • B. Lou
    Lou is a supporting character in the romantic drama film "Stuck in Love," involved in the intertwined relationships and personal struggles of a family of writers.
  • C. Lou
    Lou is a common diminutive form of the given name Louise.
  • D. Lou
    Lou is a recurring Springfield police officer on the animated television series "The Simpsons," known as Chief Wiggum’s level-headed, deadpan partner.
  • E. Lou
    Lou is a central character in the Canadian romantic drama film "Take This Waltz," which explores themes of love, fidelity, and emotional restlessness.
  • 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: Lou
Triple: [The Continental, character, Lou]
Generated description
Lou is a character in the John Wick universe’s TV series "The Continental," involved in the gritty underworld surrounding the infamous assassin hotel.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lou
Target entity description: Lou is a character in the John Wick universe’s TV series "The Continental," involved in the gritty underworld surrounding the infamous assassin hotel.
  • A. Lou chosen
    Lou is a character in the television miniseries "The Continental: From the World of John Wick," set in the action-packed criminal underworld of the John Wick franchise.
  • B. Lou
    Lou is a supporting character in the romantic drama film "Stuck in Love," involved in the intertwined relationships and personal struggles of a family of writers.
  • C. Lou
    Lou is a central character in the Canadian romantic drama film "Take This Waltz," which explores themes of love, fidelity, and emotional restlessness.
  • D. Lou
    Lou is a character from the virtual reality co-op shooter game "After the Fall," set in a post-apocalyptic, frozen Los Angeles overrun by mutated creatures.
  • E. Lou
    Lou is a recurring Springfield police officer on the animated television series "The Simpsons," known as Chief Wiggum’s level-headed, deadpan partner.
  • 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_69d81c5f739081908bc05b2461f54828 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2cf28df081908d897d7b9ec7939d completed April 14, 2026, 12:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7ce865ab4819088221189344b3801 completed May 3, 2026, 10:39 p.m.
NEDg Description generation batch_69f9fd5b82f48190b0b89ddca25883cc completed May 5, 2026, 2:23 p.m.
NED2 Entity disambiguation (via description) batch_69f9fea0a9dc8190b5b65dfec9626949 completed May 5, 2026, 2:28 p.m.
Created at: April 9, 2026, 10:17 p.m.