Triple
T11093995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Good Nurse |
E262326
|
entity |
| Predicate | editor |
P1954
|
FINISHED |
| Object |
Adam Nielsen
Adam Nielsen is a film editor best known for his work on the crime drama film "The Good Nurse."
|
E904360
|
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: Adam Nielsen | Statement: [The Good Nurse, editor, Adam Nielsen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Adam Nielsen Context triple: [The Good Nurse, editor, Adam Nielsen]
-
A.
Jesper Rasmussen
Jesper Rasmussen is a Danish professional footballer known for playing as a forward in Denmark’s top leagues.
-
B.
Rasmus Hedegaard
Rasmus Hedegaard is a Danish DJ and music producer known for his electronic and pop-oriented remixes and collaborations.
-
C.
Killian Marcus Nielsen
Killian Marcus Nielsen is one of the children of Danish actress and model Brigitte Nielsen.
-
D.
Brandt Andersen
Brandt Andersen is an American entrepreneur and film producer best known for owning the former NBA Development League team Utah Flash and later producing socially conscious feature films.
-
E.
Rasmus Christensen
Rasmus Christensen is a Danish professional footballer known for playing as a defender in European club competitions.
- 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: Adam Nielsen Triple: [The Good Nurse, editor, Adam Nielsen]
Generated description
Adam Nielsen is a film editor best known for his work on the crime drama film "The Good Nurse."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Adam Nielsen Target entity description: Adam Nielsen is a film editor best known for his work on the crime drama film "The Good Nurse."
-
A.
Jesper Rasmussen
Jesper Rasmussen is a Danish professional footballer known for playing as a forward in Denmark’s top leagues.
-
B.
Rasmus Hedegaard
Rasmus Hedegaard is a Danish DJ and music producer known for his electronic and pop-oriented remixes and collaborations.
-
C.
Killian Marcus Nielsen
Killian Marcus Nielsen is one of the children of Danish actress and model Brigitte Nielsen.
-
D.
Brandt Andersen
Brandt Andersen is an American entrepreneur and film producer best known for owning the former NBA Development League team Utah Flash and later producing socially conscious feature films.
-
E.
Rasmus Christensen
Rasmus Christensen is a Danish professional footballer known for playing as a defender in European club competitions.
- 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_69d6aa9a40d88190a373e2c7e48285db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d799ed12d88190a4ad8c346d68f11f |
completed | April 9, 2026, 12:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3e7e0c7e4819098e690ffebbd8e61 |
completed | April 18, 2026, 8:21 p.m. |
| NEDg | Description generation | batch_69e3f2cbb4708190a328cff473104d14 |
completed | April 18, 2026, 9:08 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e3f497a01881909d1dae70a02e5f97 |
completed | April 18, 2026, 9:16 p.m. |
Created at: April 8, 2026, 9:27 p.m.