Triple
T18820966
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Mexican |
E460261
|
entity |
| Predicate | cinematography |
P1953
|
FINISHED |
| Object | Dariusz Wolski |
—
|
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: Dariusz Wolski | Statement: [The Mexican, cinematography, Dariusz Wolski]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dariusz Wolski Context triple: [The Mexican, cinematography, Dariusz Wolski]
-
A.
Dariusz Wolski
chosen
Dariusz Wolski is a Polish cinematographer known for his work on major films such as the "Pirates of the Caribbean" series and collaborations with directors like Ridley Scott.
-
B.
Piotr Wolski
Piotr Wolski is a researcher known for co-authoring scientific work with machine learning scientist Marcin Andrychowicz.
-
C.
Dariusz Mioduski
Dariusz Mioduski is a Polish lawyer and businessman best known as the owner and top executive of the football club Legia Warsaw.
-
D.
Radosław Dobrowolski
Radosław Dobrowolski is a Polish academic and administrator who serves as the rector of Maria Curie-Skłodowska University in Lublin.
-
E.
Rafał Dutkiewicz
Rafał Dutkiewicz is a Polish politician and mathematician best known for serving as the long-time mayor of Wrocław, where he oversaw significant urban development and modernization.
- 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_69d8dcf94c288190a06dea029ae4b223 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5a6b9be988190b5e3804c39dc7dd9 |
completed | April 20, 2026, 4:08 a.m. |
Created at: April 10, 2026, 11:55 a.m.