Triple
T9319754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nina Agnes Paterson |
E224215
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Nina |
E344432
|
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: Nina | Statement: [Nina Agnes Paterson, givenName, Nina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nina Context triple: [Nina Agnes Paterson, 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.
Nina
chosen
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.
-
C.
Nita
Nita is a feminine given name commonly used as a shortened or affectionate form of longer names such as Juanita.
-
D.
Nadya
Nadya is a feminine given name, often used as a diminutive of Nadezhda in Slavic cultures.
-
E.
Nina Romina
Nina Romina is a ruthless local TV news director in the film "Nightcrawler," known for her willingness to exploit violent crime footage to boost ratings.
- 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_69ca8426d48481909596360f7791c7dd |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd358c7d348190a10fd8670d7756f5 |
completed | April 1, 2026, 3:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0e3aa178881909e773e11c3892381 |
completed | April 4, 2026, 10:10 a.m. |
Created at: March 30, 2026, 7:38 p.m.