Triple
T5944271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tina Sinatra |
E132240
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Christina |
E75185
|
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: Christina | Statement: [Tina Sinatra, givenName, Christina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Christina Context triple: [Tina Sinatra, givenName, Christina]
-
A.
Christina
chosen
Christina is a feminine given name widely used in many cultures, often associated with notable figures in entertainment, arts, and public life.
-
B.
Christiane
Christiane is the given name of Christiane Nüsslein-Volhard, the Nobel Prize–winning German developmental biologist known for her pioneering work on genetic control of embryonic development.
-
C.
Krista
Krista is a feminine given name, typically considered a variant of Christina and used in various European and English-speaking countries.
-
D.
Kristina Ceyton
Kristina Ceyton is an Australian film producer best known for her work on acclaimed horror and genre films, including the internationally recognized psychological horror movie "The Babadook."
-
E.
Christina Bailey
Christina Bailey is a mysterious and doomed young woman whose frantic plea for help sets off the dark, twisting events of the classic 1955 film noir "Kiss Me Deadly."
- 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_69c00869d3308190af89b2453e0f7546 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c03937b4a88190819a1fd63fc3d3ed |
completed | March 22, 2026, 6:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0c084fce481909c306d6eeb99066d |
completed | March 23, 2026, 4:24 a.m. |
Created at: March 22, 2026, 4:01 p.m.