Triple

T19521166
Position Surface form Disambiguated ID Type / Status
Subject The Loud House Movie E488403 entity
Predicate featuresCharacter P626 FINISHED
Object Lana Loud
Lana Loud is a tomboyish, mud-loving young girl from the animated series "The Loud House," known for her love of animals, fixing things, and her signature red baseball cap.
 E1380289 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: Lana Loud | Statement: [The Loud House Movie, featuresCharacter, Lana Loud]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lana Loud
Context triple: [The Loud House Movie, featuresCharacter, Lana Loud]
  • A. Lana
    Lana is a river in Albania that flows through the capital city of Tirana before joining the Tirana River.
  • B. Lana
    Lana is a professional wrestler and television personality best known for her time in WWE, where she appeared prominently as a manager and in-ring performer.
  • C. Lana
    Lana is the given name of actress Lana Condor, best known for starring in the "To All the Boys I've Loved Before" film series.
  • D. Lana
    Lana is a filmmaker best known as one of the Wachowski sisters, the co-creators of The Matrix film series.
  • E. Lana
    Lana is the seductive call girl who becomes the central love interest and catalyst for chaos in the 1983 film "Risky Business."
  • 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: Lana Loud
Triple: [The Loud House Movie, featuresCharacter, Lana Loud]
Generated description
Lana Loud is a tomboyish, mud-loving young girl from the animated series "The Loud House," known for her love of animals, fixing things, and her signature red baseball cap.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lana Loud
Target entity description: Lana Loud is a tomboyish, mud-loving young girl from the animated series "The Loud House," known for her love of animals, fixing things, and her signature red baseball cap.
  • A. Lana
    Lana is the ISO 15924 four-letter code used to represent the Tai Tham script in international standards.
  • B. Lana
    Lana is the given name of actress Lana Condor, best known for starring in the "To All the Boys I've Loved Before" film series.
  • C. Lana
    Lana is a filmmaker best known as one of the Wachowski sisters, the co-creators of The Matrix film series.
  • D. Lana
    Lana is a river in Albania that flows through the capital city of Tirana before joining the Tirana River.
  • E. Lana
    Lana is a professional wrestler and television personality best known for her time in WWE, where she appeared prominently as a manager and in-ring performer.
  • 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_69d8e8da8bec819081f400199491ccc3 completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e635a0b37c8190b70b7427c2e85f59 completed April 20, 2026, 2:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a07472af0b881909e5bada0955b52bb completed May 15, 2026, 4:17 p.m.
NEDg Description generation batch_6a0747f6b1d48190a3e70576ddb4feb2 completed May 15, 2026, 4:21 p.m.
NED2 Entity disambiguation (via description) batch_6a07492259488190a8c405598f9c8878 completed May 15, 2026, 4:26 p.m.
Created at: April 10, 2026, 1:40 p.m.