Triple
T10367945
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dirty Pretty Things |
E244302
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object | Juliette |
E265942
|
NE FINISHED |
How this triple was built (2 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: Juliette | Statement: [Dirty Pretty Things, character, Juliette]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Juliette Context triple: [Dirty Pretty Things, character, Juliette]
-
A.
Juliette
chosen
Juliette is a feminine given name of French origin, widely used in many countries and popularized through literature and film.
-
B.
Julietta
Julietta is a surreal three-act opera by Czech composer Bohuslav Martinů, based on Georges Neveux’s play about a man searching for a woman in a dreamlike town where people have lost their memories.
-
C.
Juliette Welfling
Juliette Welfling is a French film editor known for her work on numerous acclaimed international films, including the heist movie "Ocean's 8."
-
D.
Laetitia
Laetitia is a feminine given name of Latin origin, historically borne by figures such as the English poet and essayist Anna Laetitia Barbauld.
-
E.
Émilie
Émilie is the given first name of the French-born American actress Claudette Colbert, a major Hollywood star of the 1930s and 1940s.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d381b3e328819094b23b8edcd29b5a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e97106448190a075948e63184f47 |
completed | April 7, 2026, 11:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d89f53484881909fb976efb3882b9b |
completed | April 10, 2026, 6:57 a.m. |
Created at: April 6, 2026, noon