Triple
T10323090
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Believer |
E242688
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Daniel Balint
Daniel Balint is the conflicted neo-Nazi protagonist of the film "The Believer," whose Jewish heritage drives a profound internal struggle over faith, identity, and self-hatred.
|
E855339
|
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: Daniel Balint | Statement: [The Believer, character, Daniel Balint]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Balint Context triple: [The Believer, character, Daniel Balint]
-
A.
Daniel Marhely
Daniel Marhely is a French tech entrepreneur best known for co-founding the music streaming service Deezer.
-
B.
Andras Hamori
Andras Hamori is a film producer known for his work on various international and independent movies.
-
C.
András Nagy
András Nagy is a Hungarian biologist and stem cell researcher known for his pioneering work in embryonic stem cells and regenerative medicine.
-
D.
Laszlo Halasz
Laszlo Halasz was a Hungarian-American conductor and opera director best known as the founding director of the New York City Opera.
-
E.
Viktor Kassai
Viktor Kassai is a Hungarian football referee renowned for officiating high-profile international matches, including major UEFA and FIFA tournaments.
- 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: Daniel Balint Triple: [The Believer, character, Daniel Balint]
Generated description
Daniel Balint is the conflicted neo-Nazi protagonist of the film "The Believer," whose Jewish heritage drives a profound internal struggle over faith, identity, and self-hatred.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Daniel Balint Target entity description: Daniel Balint is the conflicted neo-Nazi protagonist of the film "The Believer," whose Jewish heritage drives a profound internal struggle over faith, identity, and self-hatred.
-
A.
Daniel Marhely
Daniel Marhely is a French tech entrepreneur best known for co-founding the music streaming service Deezer.
-
B.
Andras Hamori
Andras Hamori is a film producer known for his work on various international and independent movies.
-
C.
András Nagy
András Nagy is a Hungarian biologist and stem cell researcher known for his pioneering work in embryonic stem cells and regenerative medicine.
-
D.
Laszlo Halasz
Laszlo Halasz was a Hungarian-American conductor and opera director best known as the founding director of the New York City Opera.
-
E.
Viktor Kassai
Viktor Kassai is a Hungarian football referee renowned for officiating high-profile international matches, including major UEFA and FIFA tournaments.
- 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_69d381af787481908bc401325c760a88 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d6cdb6cc8190b37ca4494287128b |
completed | April 7, 2026, 10:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d71da2053481908fe5ed097b480cdd |
completed | April 9, 2026, 3:31 a.m. |
| NEDg | Description generation | batch_69d731887d2081908e6b4e33d400582f |
completed | April 9, 2026, 4:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d7329189708190bbd21bd40ec029b0 |
completed | April 9, 2026, 5:01 a.m. |
Created at: April 6, 2026, 11:50 a.m.