Triple
T10592609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I, Tina |
E250028
|
entity |
| Predicate | title |
P38
|
FINISHED |
| Object | I, Tina |
E250028
|
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: I, Tina | Statement: [I, Tina, title, I, Tina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: I, Tina Context triple: [I, Tina, title, I, Tina]
-
A.
Talky Tina
Talky Tina is the sinister talking doll from the classic TV series "The Twilight Zone," known for terrorizing a man who mistreats her owner.
-
B.
I, Tina (autobiography)
chosen
"I, Tina" is the candid autobiography of legendary singer Tina Turner, chronicling her rise to fame, turbulent marriage to Ike Turner, and ultimate journey to independence and empowerment.
-
C.
Tina
Tina is a character portrayed by actress and comedian Melissa Rauch, known for her energetic and distinctive vocal performances.
-
D.
Tina
Tina is the nickname of Tina Fey, an American comedian, writer, actress, and producer best known for her work on Saturday Night Live and 30 Rock.
-
E.
Tina
Tina is a fictional character portrayed by American actress Idara Victor.
- 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_69d381c9d3d48190a29ee491e1696a0e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5277da8048190add007ca0c37253e |
completed | April 7, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d95e85c49c8190a580536be07c0405 |
completed | April 10, 2026, 8:33 p.m. |
Created at: April 6, 2026, 12:40 p.m.