Triple
T9381528
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tina Maze |
E225795
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Tina |
E475921
|
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: Tina | Statement: [Tina Maze, givenName, Tina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tina Context triple: [Tina Maze, givenName, Tina]
-
A.
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.
-
B.
Tina
Tina, formally known as Baroness Stowell of Beeston, is a British Conservative politician and life peer in the House of Lords.
-
C.
Tina
Tina is a fictional character portrayed by American actress Idara Victor.
-
D.
Tina
Tina is a character portrayed by actress and comedian Melissa Rauch, known for her energetic and distinctive vocal performances.
-
E.
Tina
chosen
Tina is a feminine given name commonly used in English-speaking countries, often as a diminutive of names like Christina, Martina, or Valentina.
- 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_69ca842e9dcc8190a264119e683cfe04 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd50be52248190bc7cd9deb95a1ef8 |
completed | April 1, 2026, 5:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0f4311a548190885d82167199221b |
completed | April 4, 2026, 11:21 a.m. |
Created at: March 30, 2026, 7:44 p.m.