Triple
T7646044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sofia |
E173123
|
entity |
| Predicate | variantOf |
P4680
|
FINISHED |
| Object | Sophia |
E115368
|
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: Sophia | Statement: [Sofia, variantOf, Sophia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sophia Context triple: [Sofia, variantOf, Sophia]
-
A.
Sophia
Sophia of the Palatinate was a 17th-century German princess and Electress of Hanover, best known as the mother of King George I of Great Britain and a key figure in the Protestant succession to the British throne.
-
B.
Sophia
chosen
Sophia is a philosophical and theological concept signifying divine wisdom, often personified and associated with the rational principle of the cosmos.
-
C.
Sophia
"Sophia" is a lesser-known literary work by British novelist Anthony Hope, best known for his adventure classic "The Prisoner of Zenda."
-
D.
Sophia
Sophia is the young, unhappily married woman at the center of the historical romance and art-themed drama in "Tulip Fever."
-
E.
Sophia
Sophia is a small town located in Raleigh County in the southern part of West Virginia, United States.
- 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_69c6995360188190968ee57b72a1627f |
completed | March 27, 2026, 2:50 p.m. |
| NER | Named-entity recognition | batch_69c6faf3bd388190a8cb0f13322a7c00 |
completed | March 27, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8a21878508190822ff6f4681bc63d |
completed | March 29, 2026, 3:52 a.m. |
Created at: March 27, 2026, 3:58 p.m.