Triple
T28298477
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adriana Lecouvreur (Adriana) |
E713634
|
entity |
| Predicate | causeOfDeathInOpera |
P144
|
FINISHED |
| Object | poisoned violets |
—
|
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: poisoned violets | Statement: [Adriana Lecouvreur (Adriana), causeOfDeathInOpera, poisoned violets]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: causeOfDeathInOpera Context triple: [Adriana Lecouvreur (Adriana), causeOfDeathInOpera, poisoned violets]
-
A.
diesInOpera
Indicates that an entity (typically a character) dies during the events of an opera.
-
B.
causeOfDeath
chosen
Indicates the specific factor, event, or condition that directly resulted in an entity’s death.
-
C.
reasonForDeath
Indicates the cause, circumstance, or condition that led to an entity’s death.
-
D.
associatedOpera
Indicates that there is a relationship linking an entity to an opera with which it is connected or related (e.g., as subject, inspiration, or context).
-
E.
legendaryCauseOfDeath
Indicates a legendary or mythic account of how an entity died, as opposed to a historically verified cause of death.
- 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_69efb524ab688190a1ce7ee7c9520932 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69f6c49627908190b3553474c7c3072b |
completed | May 3, 2026, 3:44 a.m. |
| PD | Predicate disambiguation | batch_69f6c3f23ae081909a52801266063a3c |
completed | May 3, 2026, 3:41 a.m. |
Created at: April 27, 2026, 11:33 p.m.