Triple
T10259061
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Two Gentlemen of Verona |
E240546
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Silvia |
E169305
|
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: Silvia | Statement: [The Two Gentlemen of Verona, mainCharacter, Silvia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Silvia Context triple: [The Two Gentlemen of Verona, mainCharacter, Silvia]
-
A.
Silvia
chosen
Silvia is a feminine given name used in various languages, often associated with the Latin word for "forest" or "woods."
-
B.
Leonora
Leonora is a remote mining town in Western Australia’s Goldfields-Esperance region, historically significant for its goldfields and outback heritage.
-
C.
Leonora
Leonora is a feminine given name used in various cultures, often considered a variant of Eleanor or Leonore.
-
D.
Rosabella
Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
-
E.
Luciana
Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d24e94e08190ad2b9733bf621fe4 |
completed | April 7, 2026, 9:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d71cdcdf8c81909f5a2be1c3aa03a0 |
completed | April 9, 2026, 3:28 a.m. |
Created at: April 6, 2026, 11:31 a.m.