Triple
T10355443
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carlito's Way |
E243987
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Kleinfeld
Kleinfeld is a corrupt, cocaine-addicted lawyer in the crime film "Carlito's Way," whose reckless actions help drive the story’s tragic downfall.
|
E858025
|
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: Kleinfeld | Statement: [Carlito's Way, mainCharacter, Kleinfeld]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kleinfeld Context triple: [Carlito's Way, mainCharacter, Kleinfeld]
-
A.
Biesenthal
Biesenthal is a small town in the Barnim district of Brandenburg, Germany, known for its surrounding lakes, forests, and location within the Barnim Nature Park.
-
B.
Kurnitz
Kurnitz is a surname most notably associated with American playwright and screenwriter Harry Kurnitz.
-
C.
Friedberg
Friedberg is a historic German town in the state of Hesse, known for its medieval fortifications and strategic importance during the Seven Years' War.
-
D.
Miltenberg
Miltenberg is a historic town in Bavaria, Germany, known for its well-preserved medieval old town along the Main River and its timber-framed architecture.
-
E.
Lilienthal
Lilienthal is a German-origin surname borne by various notable individuals, including figures in aviation, science, and public service.
- 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: Kleinfeld Triple: [Carlito's Way, mainCharacter, Kleinfeld]
Generated description
Kleinfeld is a corrupt, cocaine-addicted lawyer in the crime film "Carlito's Way," whose reckless actions help drive the story’s tragic downfall.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kleinfeld Target entity description: Kleinfeld is a corrupt, cocaine-addicted lawyer in the crime film "Carlito's Way," whose reckless actions help drive the story’s tragic downfall.
-
A.
Biesenthal
Biesenthal is a small town in the Barnim district of Brandenburg, Germany, known for its surrounding lakes, forests, and location within the Barnim Nature Park.
-
B.
Kurnitz
Kurnitz is a surname most notably associated with American playwright and screenwriter Harry Kurnitz.
-
C.
Friedberg
Friedberg is a historic German town in the state of Hesse, known for its medieval fortifications and strategic importance during the Seven Years' War.
-
D.
Miltenberg
Miltenberg is a historic town in Bavaria, Germany, known for its well-preserved medieval old town along the Main River and its timber-framed architecture.
-
E.
Lilienthal
Lilienthal is a German-origin surname borne by various notable individuals, including figures in aviation, science, and public service.
- 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_69d381b22b8c8190aaed476be5f872a9 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e953d4888190b7ca0ac932349dbf |
completed | April 7, 2026, 11:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d750a9b4188190a8ecdd9e4d97570b |
completed | April 9, 2026, 7:09 a.m. |
| NEDg | Description generation | batch_69d7618da0188190901026dd51ceaa46 |
completed | April 9, 2026, 8:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d77045ea988190bd8e31f5f636f69b |
completed | April 9, 2026, 9:24 a.m. |
Created at: April 6, 2026, 11:58 a.m.