Triple
T1480372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sylvia |
E30938
|
entity |
| Predicate | isCognateWith |
P2527
|
FINISHED |
| Object | Silvie |
E170253
|
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: Silvie | Statement: [Sylvia, isCognateWith, Silvie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Silvie Context triple: [Sylvia, isCognateWith, Silvie]
-
A.
Sylvie
chosen
Sylvie is a feminine given name, often used as a French variant of Sylvia, associated with meanings related to the forest or woods.
-
B.
Silvia
Silvia is a feminine given name used in various languages, often associated with the Latin word for "forest" or "woods."
-
C.
Lucile
Lucile is a feminine given name of Latin origin, commonly associated with the name Lucille and meaning "light."
-
D.
Valeria
Valeria was a Roman imperial princess and later empress, best known as the daughter of Emperor Diocletian and for her tragic fate during the political turmoil of the Tetrarchy.
-
E.
Valeria
Valeria is the clever, sharp-tongued heroine of George Farquhar’s Restoration comedy "The Witty Fair One."
- 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_69a498fe55a88190ab7f9e40ace88e49 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c674cc9c819088fc9146c7a7a914 |
completed | March 1, 2026, 11:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad36ffd8808190894f2139ae12204e |
completed | March 8, 2026, 8:44 a.m. |
Created at: March 1, 2026, 8:11 p.m.