Triple

T8395411
Position Surface form Disambiguated ID Type / Status
Subject Lilly Wachowski E198040 entity
Predicate givenName P17 FINISHED
Object Lilly
Lilly Wachowski is an American filmmaker best known as one of the Wachowski sisters, the co-creators of The Matrix film series.
 E731020 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: Lilly | Statement: [Lilly Wachowski, givenName, Lilly]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lilly
Context triple: [Lilly Wachowski, givenName, Lilly]
  • A. Lilly
    Lilly is the surname of Bob Lilly, a Pro Football Hall of Fame defensive tackle best known for his career with the Dallas Cowboys.
  • B. Lillie
    Lillie is the given name of Lillie Hitchcock Coit, a famed 19th-century San Francisco socialite and patron associated with the city’s firefighting history.
  • C. Lily
    Lily is a feminine given name of English origin commonly associated with the lily flower and symbolizing purity and beauty.
  • D. Lily
    Lily is a pivotal character in the psychological thriller film "Black Swan," serving as a seductive and enigmatic rival whose presence intensifies the protagonist's descent into paranoia and self-destruction.
  • E. Lily
    Lily is a fictional character from the British television sitcom "The Rag Trade," which humorously portrays the lives and conflicts of workers in a small clothing factory.
  • 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: Lilly
Triple: [Lilly Wachowski, givenName, Lilly]
Generated description
Lilly Wachowski is an American filmmaker best known as one of the Wachowski sisters, the co-creators of The Matrix film series.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lilly
Target entity description: Lilly Wachowski is an American filmmaker best known as one of the Wachowski sisters, the co-creators of The Matrix film series.
  • A. Lilly
    Lilly is the surname of Bob Lilly, a Pro Football Hall of Fame defensive tackle best known for his career with the Dallas Cowboys.
  • B. Lillie
    Lillie is the given name of Lillie Hitchcock Coit, a famed 19th-century San Francisco socialite and patron associated with the city’s firefighting history.
  • C. Lily
    Lily is a feminine given name of English origin commonly associated with the lily flower and symbolizing purity and beauty.
  • D. Lily
    Lily is a pivotal character in the psychological thriller film "Black Swan," serving as a seductive and enigmatic rival whose presence intensifies the protagonist's descent into paranoia and self-destruction.
  • E. Lily
    Lily is a fictional character from the British television sitcom "The Rag Trade," which humorously portrays the lives and conflicts of workers in a small clothing factory.
  • 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_69ca82f816bc8190ab321c07d72208c1 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb81874d6c8190bbc0ac832d8a339d completed March 31, 2026, 8:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69cde85ee7b08190bfbcbed0edb142dd completed April 2, 2026, 3:54 a.m.
NEDg Description generation batch_69cdebfc63e8819087f5c1d588b58e21 completed April 2, 2026, 4:09 a.m.
NED2 Entity disambiguation (via description) batch_69cded77618c81909e8786ccd2f3e4b6 completed April 2, 2026, 4:15 a.m.
Created at: March 30, 2026, 6:03 p.m.