Triple
T13519704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Romeo and Juliet (1968 film) |
E322860
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Romeo Montague |
E54683
|
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 Montague | Statement: [Romeo and Juliet (1968 film), mainCharacter, Romeo Montague]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Romeo Montague Context triple: [Romeo and Juliet (1968 film), mainCharacter, Romeo Montague]
-
A.
Romeo Montague
chosen
Romeo Montague is the passionate young lover and tragic protagonist of William Shakespeare’s play "Romeo and Juliet," whose forbidden romance ends in mutual death.
-
B.
Romeo
Romeo is a small statutory town located in Conejos County in southern Colorado, United States.
-
C.
Romeo
Romeo is a tough, wisecracking Orbital Drop Shock Trooper and member of the Rookie’s squad in the video game Halo 3: ODST.
-
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.
Lord Capulet
Lord Capulet is Juliet’s authoritative and temperamental father in Shakespeare’s tragedy, whose decisions and conflicts help drive the lovers toward their fatal end.
- 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_69d80766a21881909f21a1b7421d3b8a |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbafa3df0c8190804174695587f0ea |
completed | April 12, 2026, 2:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75d93a2608190a3a693bf4086a010 |
completed | May 3, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:44 p.m.