Triple
T1544602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aloysia |
E32946
|
entity |
| Predicate | hasAlternativeSpelling |
P457
|
FINISHED |
| Object |
Aloysya
Aloysya is a given name, typically a feminine variant of Aloysius, used in various cultures and languages.
|
E178289
|
NE FINISHED |
How this triple was built (4 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: Aloysya | Statement: [Aloysia, hasAlternativeSpelling, Aloysya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aloysya Context triple: [Aloysia, hasAlternativeSpelling, Aloysya]
-
A.
Tatyana
Tatyana is a feminine given name of Slavic origin, particularly common in Russian-speaking countries.
-
B.
Olga
Olga is a female given name of Russian origin, historically borne by several notable figures including Russian grand duchesses and saints.
-
C.
Nadezhda
Nadezhda is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and meaning "hope."
-
D.
Sonya
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.
-
E.
Nina
Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Aloysya Triple: [Aloysia, hasAlternativeSpelling, Aloysya]
Generated description
Aloysya is a given name, typically a feminine variant of Aloysius, used in various cultures and languages.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Aloysya Target entity description: Aloysya is a given name, typically a feminine variant of Aloysius, used in various cultures and languages.
-
A.
Tatyana
Tatyana is a feminine given name of Slavic origin, particularly common in Russian-speaking countries.
-
B.
Olga
Olga is a female given name of Russian origin, historically borne by several notable figures including Russian grand duchesses and saints.
-
C.
Nadezhda
Nadezhda is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and meaning "hope."
-
D.
Sonya
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.
-
E.
Nina
Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
- F. None of above. chosen
Provenance (5 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_69a885ed29088190a3c2d5a3d100c16e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90842a4788190a812b48987503624 |
completed | March 5, 2026, 4:36 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad4018f4c08190ad1994389b4e244c |
completed | March 8, 2026, 9:23 a.m. |
| NEDg | Description generation | batch_69ad40b363bc819098a6d80f07cc80ce |
completed | March 8, 2026, 9:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad40fee2348190a048e97bfd747267 |
completed | March 8, 2026, 9:27 a.m. |
Created at: March 4, 2026, 7:26 p.m.