Triple
T3261838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nina Nevelson |
E68428
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Nina
Nina is a feminine given name used in various cultures, often as a short form of names like Antonina or Giannina, and borne by numerous notable figures in the arts and public life.
|
E344432
|
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: Nina | Statement: [Nina Nevelson, givenName, Nina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nina Context triple: [Nina Nevelson, givenName, Nina]
-
A.
Nina
Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
-
B.
Nora
Nora is a feminine given name of Latin origin, often used independently or as a diminutive of names like Honora, Eleanor, or Leonora.
-
C.
Natalia
Natalia was a short-lived Boer republic established in the 1830s in what is now KwaZulu-Natal, South Africa.
-
D.
Tamara
Tamara is a feminine given name of Hebrew origin, commonly used in various cultures and languages.
-
E.
Natalya
Natalya is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and derived from the Latin name Natalia.
- 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: Nina Triple: [Nina Nevelson, givenName, Nina]
Generated description
Nina is a feminine given name used in various cultures, often as a short form of names like Antonina or Giannina, and borne by numerous notable figures in the arts and public life.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nina Target entity description: Nina is a feminine given name used in various cultures, often as a short form of names like Antonina or Giannina, and borne by numerous notable figures in the arts and public life.
-
A.
Nina
Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
-
B.
Nora
Nora is a feminine given name of Latin origin, often used independently or as a diminutive of names like Honora, Eleanor, or Leonora.
-
C.
Natalia
Natalia was a short-lived Boer republic established in the 1830s in what is now KwaZulu-Natal, South Africa.
-
D.
Tamara
Tamara is a feminine given name of Hebrew origin, commonly used in various cultures and languages.
-
E.
Natalya
Natalya is a feminine given name of Slavic origin, commonly used in Russian-speaking countries and derived from the Latin name Natalia.
- 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_69ad8590444081909e8107a8aeef3a23 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adafa7fea08190b089b6174fd7cd32 |
completed | March 8, 2026, 5:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2e8369b708190aeddf21dd9440d6a |
completed | March 12, 2026, 4:22 p.m. |
| NEDg | Description generation | batch_69b2e9c808188190b681557e010ce159 |
completed | March 12, 2026, 4:28 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b2ea5d38808190a11ebcad2c384db7 |
completed | March 12, 2026, 4:31 p.m. |
Created at: March 8, 2026, 3:09 p.m.