Triple
T15822916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sophia |
E383656
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Sofía |
E1135613
|
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: Sofía | Statement: [Sophia, hasVariant, Sofía]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sofía Context triple: [Sophia, hasVariant, Sofía]
-
A.
Sofía
Sofía is a central fictional character in Alejo Carpentier’s novel "El siglo de las luces," embodying the personal and ideological upheavals of the Caribbean during the era of the French Revolution.
-
B.
Sofía
Sofía is the given name of Colombian-American actress and model Sofía Vergara, best known for her role on the television series "Modern Family."
-
C.
Sofía
Sofía is the enigmatic and alluring woman at the center of the psychological and romantic tensions in the Spanish film "Open Your Eyes."
-
D.
Sofía
chosen
Sofía is a feminine given name of Greek origin, widely used in Spanish-speaking countries and meaning "wisdom."
-
E.
Belén
Belén is a town in northwestern Argentina known for its traditional weaving and role as a regional center in Catamarca Province.
- 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_69d86da34c888190976e06c4019d415a |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0c4a96b848190845cf547034a24f2 |
completed | April 16, 2026, 11:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff999bba548190a39adc2d0e11c605 |
completed | May 9, 2026, 8:31 p.m. |
Created at: April 10, 2026, 4:49 a.m.