Triple
T13519503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nurse (Romeo and Juliet) |
E322855
|
entity |
| Predicate | relationshipToJuliet |
P38921
|
FINISHED |
| Object | nurse |
—
|
LITERAL 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: nurse | Statement: [Nurse (Romeo and Juliet), relationshipToJuliet, nurse]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToJuliet Context triple: [Nurse (Romeo and Juliet), relationshipToJuliet, nurse]
-
A.
roleInRomeoAndJuliet
Indicates the specific character or part that an entity plays in the work "Romeo and Juliet."
-
B.
relationshipToJulie
Indicates a specified type of relationship or connection that an entity has to Julie.
-
C.
relationshipToOdette
Indicates the specific familial, social, or interpersonal connection that an entity has with Odette.
-
D.
relationshipToPrincess
Indicates the specific familial, social, or romantic connection that one entity has to a princess.
-
E.
relationshipToCharacter
chosen
Indicates the specific type of personal, social, or narrative connection that one entity has to a given character.
- F. None of above.
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_69dbafa27f048190bed33a98e28c8d09 |
completed | April 12, 2026, 2:43 p.m. |
| PD | Predicate disambiguation | batch_69dbae0b63748190b5e207f84b2532ea |
completed | April 12, 2026, 2:36 p.m. |
Created at: April 9, 2026, 9:44 p.m.