Triple
T3668826
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Troy (film) |
E77827
|
entity |
| Predicate | leadActor |
P1507
|
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: [Troy (film), leadActor, Diane Kruger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diane Kruger Context triple: [Troy (film), leadActor, 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.
Bérénice Marlohe
Bérénice Marlohe is a French actress best known internationally for her role as Sévérine in the James Bond film "Skyfall."
-
E.
Jeanne Tripplehorn
Jeanne Tripplehorn is an American actress known for her film debut in the thriller "Basic Instinct" and prominent roles in movies like "The Firm" and the TV series "Big Love."
- 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_69ad85e083008190b2e1b7085fe500bd |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc42997d88190bc765559bd7645fc |
completed | March 8, 2026, 6:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4884e36108190a19887e81921fe32 |
completed | March 13, 2026, 9:57 p.m. |
Created at: March 8, 2026, 3:25 p.m.