Triple

T16640353
Position Surface form Disambiguated ID Type / Status
Subject Debbie Ocean E404315 entity
Predicate hasAccomplice P21638 FINISHED
Object Lou
Lou is a skilled and resourceful partner-in-crime who helps mastermind the heist in the film "Ocean's 8."
E1224347 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: [Debbie Ocean, hasAccomplice, Lou]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lou
Context triple: [Debbie Ocean, hasAccomplice, 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 the protagonist of the film "Love Lies Bleeding," a determined and emotionally complex character whose choices drive the story’s dark, romantic crime narrative.
  • D. Lou
    Lou is a common diminutive form of the given name Louise.
  • 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. 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: [Debbie Ocean, hasAccomplice, Lou]
Generated description
Lou is a skilled and resourceful partner-in-crime who helps mastermind the heist in the film "Ocean's 8."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Lou
Target entity description: Lou is a skilled and resourceful partner-in-crime who helps mastermind the heist in the film "Ocean's 8."
  • A. Lou
    Lou is the protagonist of the film "Love Lies Bleeding," a determined and emotionally complex character whose choices drive the story’s dark, romantic crime narrative.
  • B. Lou
    Lou is a recurring Springfield police officer on the animated television series "The Simpsons," known as Chief Wiggum’s level-headed, deadpan partner.
  • C. Lou
    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.
  • D. Lou
    Lou is a central character in the Canadian romantic drama film "Take This Waltz," which explores themes of love, fidelity, and emotional restlessness.
  • E. 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.
  • 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_69d8838a41f08190b0c3f79c47df5078 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e37ad0e5408190aef8b5577be73057 completed April 18, 2026, 12:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a007dc41638819090e967ade46d35a4 completed May 10, 2026, 12:44 p.m.
NEDg Description generation batch_6a007e28aee48190873c76743aa1778e completed May 10, 2026, 12:46 p.m.
NED2 Entity disambiguation (via description) batch_6a007f3bf6e081908554238d069d9abc completed May 10, 2026, 12:51 p.m.
Created at: April 10, 2026, 5:18 a.m.