Triple
T11422620
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sergio Rubini |
E270661
|
entity |
| Predicate | actedIn |
P1668
|
FINISHED |
| Object |
Denti
Denti is an Italian film featuring actor and director Sergio Rubini, known for its darkly comic exploration of relationships and personal neuroses.
|
E924583
|
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: Denti | Statement: [Sergio Rubini, actedIn, Denti]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Denti Context triple: [Sergio Rubini, actedIn, Denti]
-
A.
Dent
Dent is a surname most prominently associated with Richard Dent, a Hall of Fame former NFL defensive end for the Chicago Bears.
-
B.
Zahniser
Zahniser is a surname most notably associated with Howard Zahniser, the American environmentalist and principal author of the U.S. Wilderness Act.
-
C.
Zahn
Zahn is a surname most prominently associated with American actor and comedian Steve Zahn, known for his roles in films like "That Thing You Do!" and "Saving Silverman."
-
D.
Muldental
Muldental is the valley region surrounding the Mulde River in Germany, known for its scenic landscapes and small towns.
-
E.
Cái Răng
Cái Răng is an urban district of Cần Thơ in Vietnam’s Mekong Delta, known for its bustling floating market and river-based commerce.
- 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: Denti Triple: [Sergio Rubini, actedIn, Denti]
Generated description
Denti is an Italian film featuring actor and director Sergio Rubini, known for its darkly comic exploration of relationships and personal neuroses.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Denti Target entity description: Denti is an Italian film featuring actor and director Sergio Rubini, known for its darkly comic exploration of relationships and personal neuroses.
-
A.
Dent
Dent is a surname most prominently associated with Richard Dent, a Hall of Fame former NFL defensive end for the Chicago Bears.
-
B.
Zahniser
Zahniser is a surname most notably associated with Howard Zahniser, the American environmentalist and principal author of the U.S. Wilderness Act.
-
C.
Zahn
Zahn is a surname most prominently associated with American actor and comedian Steve Zahn, known for his roles in films like "That Thing You Do!" and "Saving Silverman."
-
D.
Muldental
Muldental is the valley region surrounding the Mulde River in Germany, known for its scenic landscapes and small towns.
-
E.
Cái Răng
Cái Răng is an urban district of Cần Thơ in Vietnam’s Mekong Delta, known for its bustling floating market and river-based commerce.
- 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_69d6aaddeaa8819088b30ef7b50598c9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d801b357e88190ace56d36a945688f |
completed | April 9, 2026, 7:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5b8a1e88c8190994bea88a0490e60 |
completed | April 20, 2026, 5:24 a.m. |
| NEDg | Description generation | batch_69e5c28e2dd481909b45a43b5825f393 |
completed | April 20, 2026, 6:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e5c4722c348190a4c49edb1f6df240 |
completed | April 20, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:34 p.m.