Triple
T18575207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Snowden (2016 film) |
E453967
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object | Alex Marquez |
—
|
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: Alex Marquez | Statement: [Snowden (2016 film), editor, Alex Marquez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alex Marquez Context triple: [Snowden (2016 film), editor, Alex Marquez]
-
A.
Alex Marquez
chosen
Alex Marquez is a film editor known for his work on projects such as the 2016 biographical thriller "Snowden."
-
B.
Álex Márquez
Álex Márquez is a Spanish Grand Prix motorcycle racer and Moto3 and Moto2 World Champion who competes in the premier MotoGP class.
-
C.
Johann Zarco
Johann Zarco is a French Grand Prix motorcycle racer best known for winning multiple Moto2 World Championships and later competing in the MotoGP premier class.
-
D.
Aleix Espargaró
Aleix Espargaró is a Spanish Grand Prix motorcycle road racer known for his long-standing presence in MotoGP and his role in developing Aprilia’s competitiveness in the premier class.
-
E.
Pol Espargaró
Pol Espargaró is a Spanish Grand Prix motorcycle road racer known for competing in the MotoGP World Championship with multiple factory and satellite teams.
- 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_69d8d38974308190a9174430ef256b73 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e543c8c0608190afc99235006bf87f |
completed | April 19, 2026, 9:06 p.m. |
Created at: April 10, 2026, 11:43 a.m.