Triple
T14293267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kalifornia |
E354370
|
entity |
| Predicate | director |
P255
|
FINISHED |
| Object | Dominic Sena |
E254605
|
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: Dominic Sena | Statement: [Kalifornia, director, Dominic Sena]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dominic Sena Context triple: [Kalifornia, director, Dominic Sena]
-
A.
Dominic Sena
chosen
Dominic Sena is an American film director best known for stylish action and thriller movies such as "Gone in 60 Seconds" and "Swordfish."
-
B.
Dominic Tierney
Dominic Tierney is a political scientist and author known for his analysis of U.S. foreign policy and military interventions.
-
C.
Dominic Anciano
Dominic Anciano is a British writer, director, and producer best known for his work in television comedy and film, including co-creating influential UK comedy projects.
-
D.
Dominic Sessa
Dominic Sessa is an American actor best known for his breakout role in the acclaimed 2023 film "The Holdovers."
-
E.
Daniel McDermott
Daniel McDermott is a person notable enough to be recognized as a namesake of the surname McDermott.
- 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_69d8278e17088190b328c5a9d4be74ff |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de7179368081908117a9ccfbf94fd4 |
completed | April 14, 2026, 4:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5504dc6c8190a4d8a5985632901d |
completed | May 8, 2026, 3:14 a.m. |
Created at: April 10, 2026, 1:11 a.m.