Triple
T13418310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Lego Movie 2: The Second Part |
E313271
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Lucy
Lucy, also known as Wyldstyle, is a tough, quick-thinking Master Builder and one of the central heroes in The Lego Movie franchise.
|
E367179
|
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: Lucy | Statement: [The Lego Movie 2: The Second Part, mainCharacter, Lucy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lucy Context triple: [The Lego Movie 2: The Second Part, mainCharacter, Lucy]
-
A.
Lucy
Lucy Hawking is a British journalist, novelist, and educator best known for her children’s science books co-written with her father, physicist Stephen Hawking.
-
B.
Lucy
Lucy is the given name of Lucy Flucker Knox, the wife of American Revolutionary War General Henry Knox and a notable figure in early American history.
-
C.
Lucy
Lucy is a fictional lion character, likely depicted with anthropomorphic traits in a narrative or animated context.
-
D.
Lucy
Lucy is a NASA Discovery Program space mission designed to study Jupiter’s Trojan asteroids to better understand the early solar system’s formation and evolution.
-
E.
Lucy
Lucy is the dog companion in the independent drama film "Wendy and Lucy," symbolizing loyalty and hardship in the protagonist's journey.
- 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: Lucy Triple: [The Lego Movie 2: The Second Part, mainCharacter, Lucy]
Generated description
Lucy, also known as Wyldstyle, is a tough, quick-thinking Master Builder and one of the central heroes in The Lego Movie franchise.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lucy Target entity description: Lucy, also known as Wyldstyle, is a tough, quick-thinking Master Builder and one of the central heroes in The Lego Movie franchise.
-
A.
Lucy
chosen
Lucy, better known by her nickname Wyldstyle, is a rebellious and resourceful Master Builder from The Lego Movie franchise.
-
B.
Lucy
Lucy is a character portrayed by English actress Ellise Chappell, known for her work in film and television.
-
C.
Lucy
"Lucy" is a 2014 science fiction action film directed by Luc Besson, in which Scarlett Johansson plays a woman who gains extraordinary mental and physical abilities after a drug enters her system.
-
D.
Lucy
Lucy is a feminine given name of Latin origin meaning "light," commonly used in many English-speaking and European countries.
-
E.
Lucy
Lucy is a central character in the film and play "Jack Goes Boating," serving as one of the key figures around whom the story’s romantic and interpersonal dynamics revolve.
- 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_69d806ad0c44819088833ae1ec9e9690 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaeb8416c8190a00dde0917c26f51 |
completed | April 12, 2026, 2:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f73082a2548190aefc0f202b84165c |
completed | May 3, 2026, 11:24 a.m. |
| NEDg | Description generation | batch_69f7311f14988190989e319741ef0ccf |
completed | May 3, 2026, 11:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f731e24d508190a896875210be3189 |
completed | May 3, 2026, 11:30 a.m. |
Created at: April 9, 2026, 9:39 p.m.