Triple
T10790597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Lee Fitzhugh Custis |
E254567
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Virginia |
E5410
|
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: Virginia | Statement: [Mary Lee Fitzhugh Custis, placeOfBirth, Virginia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Virginia Context triple: [Mary Lee Fitzhugh Custis, placeOfBirth, Virginia]
-
A.
Virginia
chosen
Virginia is a U.S. state in the Mid-Atlantic and Southeastern regions, known for its pivotal role in American history, including being home to several early presidents and key Revolutionary and Civil War sites.
-
B.
Virginia
Virginia is a small community located within the town of Georgina in Ontario, Canada.
-
C.
Virginia
Virginia is a character in the classic French farce "Il cappello di paglia di Firenze" ("The Florentine Straw Hat"), around whom part of the play’s romantic and comedic misunderstandings revolve.
-
D.
Virginia
Virginia is a coastal township in Montserrado County, Liberia, known for its beaches and proximity to the capital, Monrovia.
-
E.
La Virginia
La Virginia is a municipality in western Colombia known for its location along the Cauca River and its role as a commercial and transport hub in the Risaralda Department.
- 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_69d6aa609f008190a294200aefcb7bd5 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d732f5b8248190a633dc44ae620a3a |
completed | April 9, 2026, 5:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dbdbc3780c819092337924e2ae90f8 |
completed | April 12, 2026, 5:52 p.m. |
Created at: April 8, 2026, 9:17 p.m.