Triple
T5854262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sophie |
E130110
|
entity |
| Predicate | hasRelatedName |
P3889
|
FINISHED |
| Object | Sonya |
E118325
|
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: Sonya | Statement: [Sophie, hasRelatedName, Sonya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sonya Context triple: [Sophie, hasRelatedName, Sonya]
-
A.
Sonya
chosen
Sonya is a gentle, selfless young woman in Leo Tolstoy’s novel "War and Peace," known for her unrequited love and quiet loyalty to the Rostov family.
-
B.
Sonia
Sonia is a central female character in the romantic comedy film "Think Like a Man," whose relationships and personal growth intersect with the movie’s ensemble cast and themes about modern dating.
-
C.
Sonia
Sonia is the given name of Sonia Gandhi, an Italian-born Indian politician and former president of the Indian National Congress.
-
D.
Natalya
Natalya is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and derived from the Latin name Natalia.
-
E.
Aloysya
Aloysya is a given name, typically a feminine variant of Aloysius, used in various cultures and languages.
- 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_69c0084de39081909eb34e6bed74215a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c035529cf88190acc547ae839950e7 |
completed | March 22, 2026, 6:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0bfd6cffc8190b65252f02055e89c |
completed | March 23, 2026, 4:21 a.m. |
Created at: March 22, 2026, 3:55 p.m.