Triple
T13854638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Contractor (2022 film) |
E333030
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Nina Hoss |
E725314
|
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: Nina Hoss | Statement: [The Contractor (2022 film), starring, Nina Hoss]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nina Hoss Context triple: [The Contractor (2022 film), starring, Nina Hoss]
-
A.
Nina Hoss
chosen
Nina Hoss is a German actress acclaimed for her powerful performances in film, television, and theater, particularly through her long-standing collaboration with director Christian Petzold.
-
B.
Nathalie Baye
Nathalie Baye is an acclaimed French actress known for her versatile performances in both art-house and mainstream cinema since the 1970s.
-
C.
Franka Potente
Franka Potente is a German actress best known internationally for her breakout role in "Run Lola Run" and her appearances in the Bourne film series.
-
D.
Sabine Ganz
Sabine Ganz is known as the spouse of the late Swiss actor Bruno Ganz, acclaimed for his roles in European cinema and theater.
-
E.
Diane Kruger
Diane Kruger is a German-born actress and former fashion model best known for her roles in films such as "Troy," "Inglourious Basterds," and "National Treasure."
- 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_69d81c5ba13c8190839315f54768acfd |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02db9c9c81909bb2d2fbfb7394b1 |
completed | April 14, 2026, 9:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7ce6c0c3c8190911b56b20c9eb955 |
completed | May 3, 2026, 10:38 p.m. |
Created at: April 9, 2026, 10:14 p.m.