Triple
T22895949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Appius and Virginia |
E568173
|
entity |
| Predicate | protagonist |
P268
|
FINISHED |
| Object | Virginia |
—
|
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: Virginia | Statement: [Appius and Virginia, protagonist, Virginia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Virginia Context triple: [Appius and Virginia, protagonist, Virginia]
-
A.
Virginia
Virginia is a small community located within the town of Georgina in Ontario, Canada.
-
B.
Virginia
chosen
Virginia is a feminine given name of Latin origin, historically associated with notions of virtue and widely used in English-speaking countries.
-
C.
Virginia
Virginia is a gold mining town in South Africa’s Free State province, known for its role in the region’s mining industry and its location near the Sand River.
-
D.
Virginia
Virginia is a semi-rural suburb in the northern Adelaide region of South Australia, known for its market gardens and greenhouse horticulture.
-
E.
Virginia
"Virginia" is a tragic play by Italian dramatist Vittorio Alfieri that dramatizes themes of tyranny, virtue, and personal sacrifice in ancient Rome.
- 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_69e2458c23ec81908fa2570692c6614f |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1801212808190b6471764d6c0b264 |
completed | April 29, 2026, 3:50 a.m. |
Created at: April 17, 2026, 3:40 p.m.