Triple
T23001888
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Romeo and Juliet: The Tomb Scene |
E572652
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Romeo |
—
|
NE NERFINISHED |
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 | Statement: [Romeo and Juliet: The Tomb Scene, mainCharacter, Romeo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Romeo Context triple: [Romeo and Juliet: The Tomb Scene, mainCharacter, Romeo]
-
A.
Romeo
Romeo is a tough, wisecracking Orbital Drop Shock Trooper and member of the Rookie’s squad in the video game Halo 3: ODST.
-
B.
Romeo
Romeo is the song that represented the host nation at the Eurovision Song Contest in 1986.
-
C.
Romeo
Romeo is a small statutory town located in Conejos County in southern Colorado, United States.
-
D.
Romeo
Romeo is a recurring mad-scientist villain in the children's animated superhero series PJ Masks, known for his inventive gadgets and schemes to outsmart the heroes.
-
E.
Roméo
chosen
Roméo is a masculine given name of Latin origin, commonly used in French and other Romance languages and best known from Shakespeare’s tragic hero in "Romeo and Juliet."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245b6a3ac81908087599eefe3e365 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18353d05481909abacb48a14ef21e |
completed | April 29, 2026, 4:04 a.m. |
Created at: April 17, 2026, 3:50 p.m.