Triple
T7950690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deliver Us from Eva |
E184605
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
B.E. Brauner
B.E. Brauner is a screenwriter best known for co-writing the romantic comedy film "Deliver Us from Eva."
|
E747970
|
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: B.E. Brauner | Statement: [Deliver Us from Eva, screenwriter, B.E. Brauner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: B.E. Brauner Context triple: [Deliver Us from Eva, screenwriter, B.E. Brauner]
-
A.
Reiner Breuer
Reiner Breuer is a German politician who serves as the mayor of the city of Neuss in North Rhine-Westphalia.
-
B.
Marten Wassmann
Marten Wassmann is an architect known for his partnership role at the Dutch architecture firm Benthem Crouwel Architekten.
-
C.
Walter Meierjohann
Walter Meierjohann is a theatre director known for his innovative, visually driven productions and leadership roles in European and UK theatre institutions.
-
D.
Joseph Weisbecker
Joseph Weisbecker was an American engineer and computer designer best known for pioneering early hobbyist microcomputers and educational computing systems in the 1970s.
-
E.
Hans Wiegel
Hans Wiegel is a prominent Dutch liberal politician who served as leader of the VVD and as Deputy Prime Minister of the Netherlands in the late 20th century.
- 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: B.E. Brauner Triple: [Deliver Us from Eva, screenwriter, B.E. Brauner]
Generated description
B.E. Brauner is a screenwriter best known for co-writing the romantic comedy film "Deliver Us from Eva."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: B.E. Brauner Target entity description: B.E. Brauner is a screenwriter best known for co-writing the romantic comedy film "Deliver Us from Eva."
-
A.
Reiner Breuer
Reiner Breuer is a German politician who serves as the mayor of the city of Neuss in North Rhine-Westphalia.
-
B.
Marten Wassmann
Marten Wassmann is an architect known for his partnership role at the Dutch architecture firm Benthem Crouwel Architekten.
-
C.
Walter Meierjohann
Walter Meierjohann is a theatre director known for his innovative, visually driven productions and leadership roles in European and UK theatre institutions.
-
D.
Joseph Weisbecker
Joseph Weisbecker was an American engineer and computer designer best known for pioneering early hobbyist microcomputers and educational computing systems in the 1970s.
-
E.
Hans Wiegel
Hans Wiegel is a prominent Dutch liberal politician who served as leader of the VVD and as Deputy Prime Minister of the Netherlands in the late 20th century.
- 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_69ca8292cba881908a64427b938dac47 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3b5b7450819091e4e6f21e9d832d |
completed | March 31, 2026, 3:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cebaee29308190a468b2bc24008428 |
completed | April 2, 2026, 6:52 p.m. |
| NEDg | Description generation | batch_69cebc9d4ca88190942c333806181b55 |
completed | April 2, 2026, 6:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cec0a9c39c8190a7bcecc6927b98f0 |
completed | April 2, 2026, 7:16 p.m. |
Created at: March 30, 2026, 5:10 p.m.