Triple
T16197197
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marina de Tavira |
E393092
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Marina |
E270577
|
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: Marina | Statement: [Marina de Tavira, givenName, Marina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marina Context triple: [Marina de Tavira, givenName, Marina]
-
A.
Marina
Marina is a recurring comedic character in the long-running British sitcom "Last of the Summer Wine," known for her flirtatious relationship with the married Howard.
-
B.
Marina
Marina is a 2012 Tamil coming-of-age drama film that helped establish Sivakarthikeyan as a leading actor in the Tamil film industry.
-
C.
Marina
Marina is the given name of Marina von Neumann Whitman, an American economist and former General Motors executive.
-
D.
Marina
chosen
Marina is a female given name of Latin origin, commonly used in various cultures and often associated with the sea.
-
E.
Marina
Marina is the eccentric, childlike porn actress who becomes the obsessive focus of a recently released psychiatric patient in Pedro Almodóvar’s dark romantic comedy film "Tie Me Up! Tie Me Down!".
- 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_69d87f1e49ac8190a311b54d32990576 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e222dace848190b1a98e47333b922b |
completed | April 17, 2026, 12:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffff0f352081908324783743e47029 |
completed | May 10, 2026, 3:44 a.m. |
Created at: April 10, 2026, 5:02 a.m.