Triple
T19901354
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tina Majorino |
E478296
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Tina Majorino |
—
|
NE NERFINISHED |
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 Majorino | Statement: [Tina Majorino, name, Tina Majorino]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tina Majorino Context triple: [Tina Majorino, name, Tina Majorino]
-
A.
Tina Majorino
chosen
Tina Majorino is an American actress known for her roles as a child and adult in films and television series such as "Waterworld," "Napoleon Dynamite," and "Veronica Mars."
-
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 character portrayed by actress and comedian Melissa Rauch, known for her energetic and distinctive vocal performances.
-
D.
Tina
Tina is a feminine given name commonly used in English-speaking countries, often as a diminutive of names like Christina, Martina, or Valentina.
-
E.
Tina
Tina is a character from the manga and anime series "Bastard!!", known for her role within its dark fantasy narrative.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e520682081909892916424699bd5 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65941977081909e2f94724eb2c3c0 |
completed | April 20, 2026, 4:50 p.m. |
Created at: April 10, 2026, 1:52 p.m.