Triple
T6463587
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Romeo Must Die |
E142178
|
entity |
| Predicate | basedOn |
P98
|
FINISHED |
| Object | Romeo and Juliet |
E62132
|
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: Romeo and Juliet | Statement: [Romeo Must Die, basedOn, Romeo and Juliet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Romeo and Juliet Context triple: [Romeo Must Die, basedOn, Romeo and Juliet]
-
A.
Romeo and Juliet
chosen
Romeo and Juliet is a tragic play by William Shakespeare about two young lovers from feuding families whose doomed relationship has become one of the most famous love stories in Western literature.
-
B.
Romeo + Juliet
Romeo + Juliet is a 1996 modernized film adaptation of Shakespeare’s tragedy, directed by Baz Luhrmann and starring Leonardo DiCaprio and Claire Danes as the titular lovers.
-
C.
Star Crossed Lovers
Star Crossed Lovers is a romantic-themed segment or track that explores the challenges and intensity of a doomed or fated love.
-
D.
Lovers
Lovers is a Spanish film featuring actress Maribel Verdú in one of her notable roles.
-
E.
Romeo
Romeo is a small statutory town located in Conejos County in southern Colorado, United States.
- 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_69c008d3bf4c8190bcf798c5ba9d6fb3 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c069f9b58081909412b9da753b9285 |
completed | March 22, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c64be78ae48190b38390ed245694fb |
completed | March 27, 2026, 9:20 a.m. |
Created at: March 22, 2026, 4:49 p.m.