Triple
T7714622
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Goodbye Bafana |
E174849
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Diane Kruger |
E31747
|
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: Diane Kruger | Statement: [Goodbye Bafana, castMember, Diane Kruger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diane Kruger Context triple: [Goodbye Bafana, castMember, Diane Kruger]
-
A.
Diane Kruger
chosen
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."
-
B.
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.
-
C.
Juliette Binoche
Juliette Binoche is an acclaimed French actress known for her nuanced performances in international cinema and her Academy Award-winning role in "The English Patient."
-
D.
Emmanuelle Béart
Emmanuelle Béart is a French actress acclaimed for her performances in films such as "Manon des Sources" and "Mission: Impossible."
-
E.
Nathalie Baye
Nathalie Baye is an acclaimed French actress known for her versatile performances in both art-house and mainstream cinema since the 1970s.
- 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_69c6995c463c8190a14458036249d419 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702ca8f048190a6ea27b8cee2f93e |
completed | March 27, 2026, 10:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8acd5e32c8190869834b21aeae8a7 |
completed | March 29, 2026, 4:38 a.m. |
Created at: March 27, 2026, 4:04 p.m.