Triple
T3237116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taxi to the Dark Side |
E67880
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object |
Eva Orner
Eva Orner is an Australian documentary filmmaker and producer known for her hard-hitting political and human rights films, including the Academy Award-winning "Taxi to the Dark Side."
|
E348397
|
NE FINISHED |
How this triple was built (4 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: Eva Orner | Statement: [Taxi to the Dark Side, producer, Eva Orner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eva Orner Context triple: [Taxi to the Dark Side, producer, Eva Orner]
-
A.
Evelyn Baran
Evelyn Baran is best known as the wife of pioneering engineer Paul Baran, a key figure in the development of packet-switched networks and the internet.
-
B.
Eva Schubach
Eva Schubach is known as a former spouse of Gerhard Schröder, the one-time Chancellor of Germany.
-
C.
Lucia Wald
Lucia Wald was the wife of renowned statistician Abraham Wald, known primarily through her association with his life and work.
-
D.
Nora Grossman
Nora Grossman is a film producer best known for her work on the acclaimed historical drama "The Imitation Game."
-
E.
Anna Eberstein
Anna Eberstein is a Swedish television producer and retail executive best known as the wife of British actor Hugh Grant.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Eva Orner Triple: [Taxi to the Dark Side, producer, Eva Orner]
Generated description
Eva Orner is an Australian documentary filmmaker and producer known for her hard-hitting political and human rights films, including the Academy Award-winning "Taxi to the Dark Side."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Eva Orner Target entity description: Eva Orner is an Australian documentary filmmaker and producer known for her hard-hitting political and human rights films, including the Academy Award-winning "Taxi to the Dark Side."
-
A.
Evelyn Baran
Evelyn Baran is best known as the wife of pioneering engineer Paul Baran, a key figure in the development of packet-switched networks and the internet.
-
B.
Eva Schubach
Eva Schubach is known as a former spouse of Gerhard Schröder, the one-time Chancellor of Germany.
-
C.
Lucia Wald
Lucia Wald was the wife of renowned statistician Abraham Wald, known primarily through her association with his life and work.
-
D.
Nora Grossman
Nora Grossman is a film producer best known for her work on the acclaimed historical drama "The Imitation Game."
-
E.
Anna Eberstein
Anna Eberstein is a Swedish television producer and retail executive best known as the wife of British actor Hugh Grant.
- F. None of above. chosen
Provenance (5 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_69ad858d27348190abb61c280b4c86a9 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaef29bf48190a9aa3a39f0138428 |
completed | March 8, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b31a65d2988190a053eb2f22503f83 |
completed | March 12, 2026, 7:56 p.m. |
| NEDg | Description generation | batch_69b31c33fc3c8190b5af3f38206736de |
completed | March 12, 2026, 8:04 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b31c9f48788190bdf4b4ce47e1e5b5 |
completed | March 12, 2026, 8:05 p.m. |
Created at: March 8, 2026, 3:08 p.m.