Triple
T14413163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 12 Rounds |
E357381
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object |
Daniel Kunka
Daniel Kunka is an American screenwriter best known for writing the action film "12 Rounds" and other high-concept genre projects.
|
E1097632
|
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 Kunka | Statement: [12 Rounds, screenwriter, Daniel Kunka]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Kunka Context triple: [12 Rounds, screenwriter, Daniel Kunka]
-
A.
Christopher Turk
Christopher Turk is a charismatic and ambitious surgeon on the TV series "Scrubs," known for his close friendship with J.D. and his comedic, energetic personality.
-
B.
Daniel Richter
Daniel Richter is a contemporary German painter renowned for his large-scale, politically charged, and vividly colored figurative works.
-
C.
Michael Ernst
Michael Ernst is a computer scientist known for his work in software engineering and programming languages, including research on type systems and software reliability.
-
D.
Nick Halsey
Nick Halsey is the down-on-his-luck, alcoholic salesman whose life unravels and forces him to start over in the dark comedy-drama film "Everything Must Go."
-
E.
Martin Obzina
Martin Obzina was a film art director known for his set design work on classic early 20th-century cinema.
- 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 Kunka Triple: [12 Rounds, screenwriter, Daniel Kunka]
Generated description
Daniel Kunka is an American screenwriter best known for writing the action film "12 Rounds" and other high-concept genre projects.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Daniel Kunka Target entity description: Daniel Kunka is an American screenwriter best known for writing the action film "12 Rounds" and other high-concept genre projects.
-
A.
Christopher Turk
Christopher Turk is a charismatic and ambitious surgeon on the TV series "Scrubs," known for his close friendship with J.D. and his comedic, energetic personality.
-
B.
Daniel Richter
Daniel Richter is a contemporary German painter renowned for his large-scale, politically charged, and vividly colored figurative works.
-
C.
Michael Ernst
Michael Ernst is a computer scientist known for his work in software engineering and programming languages, including research on type systems and software reliability.
-
D.
Nick Halsey
Nick Halsey is the down-on-his-luck, alcoholic salesman whose life unravels and forces him to start over in the dark comedy-drama film "Everything Must Go."
-
E.
Martin Obzina
Martin Obzina was a film art director known for his set design work on classic early 20th-century cinema.
- 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_69d82793421c8190861eb0e673b085de |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de90cb3c708190822f5506ebf7ee9d |
completed | April 14, 2026, 7:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd552858208190ba1550e7c1176a2a |
completed | May 8, 2026, 3:14 a.m. |
| NEDg | Description generation | batch_69fd5671e4688190ab1b7a7ed6c0cfb8 |
completed | May 8, 2026, 3:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd57710f648190a1344ac1363acce1 |
completed | May 8, 2026, 3:24 a.m. |
Created at: April 10, 2026, 1:17 a.m.